Join me today for Episode 909 of Bitcoin And . . .
Topics for today:
- Pippellia's Article
- PoW Keys
- WoT Model
- Social Graph Analysis
- Trust Metrics and More
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[00:00:00]
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Good morning. This is David Bennett, and this is Bitcoin and, a podcast where I try to find the edge effect between the worlds of Bitcoin, gaming, permaculture, podcasting, and education to gain a better understanding of all. Edge effect is a concept from ecology describing a greater diversity of life where the edges of 2 systems overlap. While species from either system can be found at the edge, it is important to note there are species in the overlap that exist in neither system, and that is what I seek to uncover. Uncover. So join me in discovering the variety of things being created as Bitcoin rubs up against other systems. It is 9:35 AM Pacific Daylight Time. It is the 18th day of June 2024, and this is episode 909 of Bitcoin. And today, I'm gonna do something completely different.
I'm going to bring you a reading of Papalia's Navigating the Social Graph. Now, I found this article, yesterday or the day before, and I kinda found it fascinating because it talks about things like web of trust and identity and denial of service attacks on your identity as somebody who's using Nostr, but it, you know, could be Facebook, it could be Twitter. This is like sort of the the social graph problem. And this piece goes into what is a social graph? And it's it's very good. However, there are there are some problems with it, not in its ideas, but in the fact that it is it's heavily weighted towards matricy math. There's some algebra.
There's using formulas to define certain terms, and I have done my best to be able to clip that out and not basically be sitting here saying a plus b equals, you know, c squared to the raised to the f sharp and all that stuff. So I've done my best to get that out of there with without destroying the central idea or the theme of this particular paper. Again, this paper is really good. And even if you don't know math, you should probably go read this paper. I will definitely be having the link to this paper in the show notes, and I highly recommend that you guys go read this because as we sail this novel river that is Nostra that we find ourselves on.
Who we follow and what types of recommendations we make it make for each other. And that includes, like, who should you follow? Should you follow somebody just because I'm following them? Or should you follow somebody until, like, wait to follow somebody until I tell you, hey, you probably should also be following this person. Is there inferences that can be drawn? If I follow somebody and you follow somebody, but we're not following each other, do we have something in common? Well, this paper is is bringing that all home. But one of the most interesting parts about this paper is when we start talking about the w o t or web of trust and proof of work whether it's proof of work that I actually wrote the note or proof of work as to whether or not I actually generated my own keys.
Like, because, like, right now I'm being spammed by followers and it it is the exact whoever wrote this bot for Noster, it's it's definitely the same person. Alright? It this is every day, I will get on to Noster, and at least 5 times during the day that I get on, I will notice that I have 27 new followers. But each one of these followers has almost the exact same avatar. It's like concentric orange circles that, more or less don't, you know, kinda don't line up, and they don't line up in certain patterns. It's sort of like think of, you know, the same clock face, but different pictures of the clock at different times. It's following only 17 people every time it follows somebody.
So every time one of these things one of the whenever this thing gets activated, it spins up a new insect and n pub key pair and it follows the exact same 17 people. So now I've got like, I don't know, it started happening when I was right around 8,000 followers, and now I'm up to almost 12,000 followers, and it's not fun because they don't mean anything to me. I don't I don't care about these things. They they they have all of them have 0 posts, of course. They only follow these certain people. They never actually do or engage with Noster or the general population of Noster in any way other than inflating our follower counts.
But what does that do to my web of trust? If something that nobody else constantly follow if if my web of trust is being diluted by a bunch of bots that don't make any sense or don't engage in any meaningful way. What does that what does that say about my account? Well, this paper goes through a lot of that stuff And I think we should probably be very, very attentive to how we start moving forward in the future because all of our trust is basically mitigated or provided by central parties, 3rd parties, to be exact, people like Elon Musk, people like Mark Zuckerberg, all these people. Right? They are providing some kind of nexus of which here's how you here's the way that you're going to interact with each other and here's the way you're going to discover each other and here's the way you're going to trust each other and I don't want any of that.
However when you're in a completely free and open environment that Nostra provides it can be a problem. We are experiencing different problems. And as we move through Noster and on into the future, how are we going to fix our little red wagon? Well, Papillia, if that is the way that I can pronounce that name, he's also known as the social graph guy. He has Noster, so I will be putting his in pub into the show notes hopefully he's gonna kind of clear some of this up or at least give us some things to think about so with no further ado here it is.
Navigating the Social Graph by Popela, the social graph guy. The emergence of decentralized social networks with sovereign identities, for example, Nostra, is a pivotal moment for the web, and it comes at the perfect time. In the age of artificial intelligence, where it's getting cheaper and cheaper to impersonate people, distort videos and images, or just make them up, how is it possible to know what's true? What does truth even mean these days? These are questions I will not attempt to answer. However, even if the ontology of truth has eluded philosophers forever, a more practical approach is needed if the web is not to degenerate into a nihilistic place where nothing is real, or worse, a place where everything requires identity verification and the approval of a central authority.
In this paper, you will find a definition of the social graph, principles for thinking about it, and practical ideas for using it for DOS prevention, social discovery, anti impersonation, accurate ratings, and more. You will also learn about alternative mechanisms that can be used in conjunction with social graph analysis to provide more compelling and complete solutions. The social graph. First, what is a graph? A graph is a mathematical structure consisting of a set of nodes or vertices and a set of edges edges are defined by a relation on a set For example, if node v and node u are related, then the pair is contained in a set.
The relationship can be symmetrical or asymmetrical, distinguishing between undirected and directed graphs. A social graph is simply a graph in which the nodes are human entities such as individuals, or companies, or groups. And the edges represent some form of social relationship such as being friends with, following, or being zapped. Before we go any further, we need to define another important notion. That of a neighborhood. A neighborhood is a set of vertices inside another set of vertices whose defining property is to be connected to a vertex in a set.
What problem is the social graph solving? The answer is many, in many different ways and in many different forms. We are talking about using it as a defense mechanism against impersonation, as a way to prevent DOS attacks, to facilitate social discovery and connections, and to calculate more accurate ratings. But before we dive into the implications, I ask you to pause for a moment to understand the magnitude of what we are discussing Principles. Don't fall into the trap of thinking that the engineers at YouTube were evil when they designed their algorithm. As the saying goes, the road to hell is paved with good intentions. If your implementation is successful, people's behavior will be affected.
That's why it's important to establish principles that can guide designers and developers in creating systems that are healthier than the world we're leaving behind. The principle of least interference. A good rule of thumb when designing systems that affect social dynamics is to follow the principle of least interference. The principle of least interference is this, solve the problem by implementing whatever mechanism has the least possible impact on social dynamics as a heuristic based on this principle it is generally better to focus on solving only one problem at a time since it is unlikely that solving multiple problems at once will not lead to a greater impact on those social dynamics.
A great example to analyze is the WOT score implemented by Coracle, which tries to reduce spam, imposters, and objectionable content all at once. Let's start by analyzing how it works as an anti impersonation mechanism. If you search for Lynn Alden, it is very likely, if you are part of a, quote, good network, that the in pub with the highest WOT is the real LIN, while all others are impostors. However, a simple warning icon above all other LINS would have accomplished the same goal without introducing a rating that can alter social dynamics. In fact, the WOT score represents the opinion of the crowd around you, which doesn't offer any nuance when comparing the reputation of different real people.
Crowd thinking is one of the biggest problems of the world that we are leaving, and it would be a silly exercise to bring it with us. Besides, we do not walk around with a number on our forehead in meat space, nor should we in cyberspace, even if that number depends on the observer. There is also another, more fundamental problem, which is that the user only has information about a limited subset of the network, as defined by the relation follow or mute. Therefore, if you set the WOT threshold above 0, you won't see anyone outside of that, which certainly prevents spam, but at the cost of discoverability. We'll come back to how to design a more effective anti impersonation system later.
Principle of relativity in cyberspace. The current web and our societies in general are built on an outdated paradigm that relies on and requires consensus about what is true, false, good, or bad. Unspeakable atrocities have been committed to achieve and maintain such consensus. However, we must move beyond this legacy of the past if we are to usher in a new era of freedom. The notion of a commonly shared belief or social truth is as dead in this new millennium as God was in the 19th century. The principle of relativity in cyberspace states, you cannot and should not dictate what is true or false, what is good content or bad content or spam for another person.
If you do, you will simply be replacing the beast with a new set of beliefs. It's true that there are many different clients to choose from, but there were many different web browsers back in the day and that didn't end well. The reality is that a global agreement on things has seldom been necessary. In fact, relative canonicality can be achieved without the need for a central authority. Wikipedia, a Nostril alternative to Wikipedia, as you have probably guessed, fully embraces this idea of relative canonicality. Each topic contains multiple entries.
There is no single source of truth that aims or claims to be neutral. Of course, this doesn't mean that all entities are equally good or useful. On the contrary, there is a very opinionated and biased way of sorting them, which depends on the observer. Regardless of the specific implementation, again, number on the forehead, this is the way to go. This is relativity in cyberspace. There is no global. The point is arguably a corollary of the previous principle. But it's so important that it needs to be fully explained. The notion of global is also dead.
Despite being the largest index in the world, Google only indexes a very small portion of the web. The engineering challenges behind indexing absolutely every person and every piece of content in an ever growing open and hostile decentralized network will likely exacerbate this outcome. Nostr will outgrow every index. As spammers become more sophisticated, crawlers will have to become more selective, limiting the percentage of the network that is indexed. Therefore, a client designer or developer, you really only have a limited view of what's happening. This reinforces the statement that you cannot decide for someone else what is good or bad. True or false?
You can't possibly know because what you have is a local limited view, just like everyone else's. Let's take Nosterband for example. As much as I appreciate their cool statistics, it's important to know their limitations. The version of trust rank that Nosterband implements, one, runs on the subset of the network that they are aware of. 2, uses an arbitrarily chosen set of NIP 0 5 providers for determining the so called good seeds. This approach is fine if it's used to defend the service provider against spammers. See DOS prevention section for improvements. However, such an arbitrary view, all are, but some less than others, shouldn't be imposed on the end user, but only used as a way for the service provider to select the customers it wants to serve.
Principle of natural patterns. I believe that technology is made for people, so it should serve us and improve our quality of life. We shouldn't adapt to technology. Technology should adapt to us. This context is no exception to the rule. Humans are social creatures and our social dynamics and behaviors have evolved through our long history. Therefore, before introducing new and artificial social dynamics, it's wiser to mimic the natural dynamics that have evolved with us for millennia. The principle of natural patterns states, when in doubt, try to mimic the social dynamics of meat space.
For example, in meat space, a friend of a friend is not a stranger, but it's not a friend either. It's something in between and a good candidate for a potential future friend. Another example is something that will hit home if you've ever been to a Bitcoin conference. When you're among people with the same interest, and I would argue mostly overlapping values, even if you've never met them, you can't say they're strangers. Examples abound, so there's really no reason to try to impose any artificial dynamics before exhausting what nature has given us.
To summarize, we take from this section 3 principles that will inform our analysis and design decisions moving forward. The principle of least interference, the principle of relativity in cyberspace, The Principle of Natural Patterns The Problems Finally, we can start discussing some concrete problems and ways to solve them. There are several well defined problems that social graph analysis can help with. Again, it's important to define them well if we want to follow the principle of least interference. The first problem we will analyze is protection against impersonation. One of the most important pieces to get right.
Anti impersonation, social endorsement. Who is the real Giacomo in this case? It's pretty simple. Pausing to describe a picture. 2 different Giacomo Zuco Twitter account banners. One states, has 68,000 followers, the other says 44. First of all, let's talk about what won't help us decide. The name, the profile picture, the bio, the date of creation. And no, not even the number of followers as this is a completely irrelevant metric in an open and hostile network. What helps us is the line below. Quote, followed by Bitcoin Africa Story, Nostril Wallet Connect, and 666 others you follow.
This is the signal. Follows can be faked, but follows from people you trust cannot be faked or are more expensive to get. This is a particularly good design because it adheres to the principle of relativity in cyberspace. It doesn't prescribe who the real Giacomo is like the aforementioned WOT score where every follower has the same weight but it shows you clues that you can decide to interpret in the way that you want the same idea can be applied to mutes which give you a few more clues. Obviously, such a system can be extended not only to the people you follow, but also to the people who are 2 hops away from you or any number of hops away from you.
However, the signal becomes increasingly diluted and unreliable the farther out you go. This approach is a good start but it can't really solve the impersonation problem on its own because it's limited in reach and suffers from a bootstrapping problem for new users which is really the same problem if you think about it There is also a subtle issue worth mentioning. What if a reputable person tries to impersonate someone else? This attack is possible because it won't be detected immediately. But the more prominent the person, the faster this attack will be detected causing the attacker to lose their hard earned reputation.
Furthermore, for truly prominent figures such as heads of state, this problem can be mitigated but not completely solved by third party credentials that can be displayed by clients. NIP 0 5 Providers. Another approach that has been tried is to rely on NIP 0 5 Providers. If I trust a NIP 0 5 provider to verify the people they serve then I can assign a non zero level of trust to their However, this approach obviously requires trust which isn't ideal since there are trustless approaches that provide better guarantees. Proof of work keys.
When we see a stranger enter the pub where we are, or when we are crowded on a subway, we find ourselves potentially vulnerable to strangers. In most civilized places, however, we don't live in a constant state of fear. And that comes from the fact that others are vulnerable as well. Everyone has skin in the game, even the aggressors, which means that the best way to play this game is not to mess with other people. On Noster however, the only consequence you can feel is the loss of your reputation, which by definition is worthless when the in pub is brand new however there is a way to add skin in the game for new keys For example, proof of work keys.
The process works like this. While creating your identity, your machine can generate a new private key until the corresponding public key, as a binary number, satisfies the following inequality the public key is less than or equal to some threshold, T, of your choice. Since there is no efficient way to do this, allegedly, this process requires the expenditure of computing power and energy. This adds a quantifiable and verifiable cost to any identity making credible impersonation expensive. However proof of work keys can't work at scale because 1, they require commitment at key creation which is a bad user experience because it reverses the try before you buy rule 2, the proof of work can't be updated over time.
And 3, it is not possible to delegate the work without delegating the key, supposedly. The last point implies that the economy of scale favors the attacker because they have multiple targets whereas the defender can only defend themself however there is a similar approach that offers what I believe are more appropriate trade offs proof of work endorsement Proof of work endorsement. A proof of work endorsement is a simple unsigned user metadata event containing a target in pub, a difficulty commitment threshold, t, and a nonce.
The nonce is randomly generated until the following condition is satisfied. Some event is less than or equal to 2 raised to the t power where the event is a cryptographic hash function such as sha256. If the condition is met, the proof of work endorsement is valid and the target npubs powwait becomes 256 minus t. What I am describing is basically NIP13, except that it should use kind 0 and it doesn't need a signature. What makes proof of work endorsements different from proof of work keys is that one, it doesn't require any commitment at key creation The proof of work can be updated over time.
It is possible to delegate the work without delegating the key. And 4, requires data availability because the work is separate from the key point 3 is what makes this approach feasible and scalable especially for mobile devices constrained by CPU and battery limitations. Point 4 isn't particularly bad because the events are small, public and the user has an incentive to store them. Example of how it works. Alice gives a n pub to the miner. Alice pays the miner $10 The miner then mines the proof of work endorsement with the target n pub.
The target n pub now has a non zero proof of work wait. How does it help? Well, if the target impub has a non zero proof of work weight, credible impersonation attempts require a cost. The attacker's social capital or proof of work weight becomes less and less valuable and is eventually lost as more people discover the attack and mute the attackers in pub. Furthermore, assuming that most of the proof of work weight is allocated to real people, it's possible to use its distribution to fix the shortcomings of social endorsement, for example.
A new user can be presented with the choice of following the inpubs with the highest trust rank where the good seeds are the inpubs with non zero proof of work wait. The same principle can be applied to an existing user trying to get information about the inpub that is many hops away from him or her. If you want to summarize this idea to the maximum, if the number of followers is meaningless that's not true for the number of followers with non zero proof of work weight I have started working on this idea and preliminary results are promising, but more thought is needed Colored Halo.
Another anti impersonation mechanism tried by Nostrudel is the use of a few characters of the public key in hex format to identify a unique color that is displayed around the user's profile picture. The idea is that if I am already following the real Lynn Alden, a credible impersonation attempt would require generating many times the key pair to get the same or very similar color. That requires energy which means cost. However, this information is only useful if I already follow the real Lynn and remember her color asking my trusted network for the color of a particular person is no different task than asking her in pub directly This means that this approach, while interesting, is not very useful.
Denial of Service Attack Prevention When analyzing denial of service attacks, we should distinguish between defense mechanisms that work at the network level, at the service provider level, and at the user level. Talking about the first case may seem to contradict the principle of relativity in cyberspace but here by network we mean any set of network participants who are willing to cooperate but not necessarily trust each other Network Level proof of work events basically apply the concept of HashCash not to email messages but to notes. There is not much to say here except to note that the proof of work can be outsourced to service providers that can most likely out compete the attackers inefficiency.
Service Provider Level. The best tool the service provider can use to prevent DOS' money. A paid service, either pay per use or subscription base, can work both for monetization and as a defense mechanism. However, not every service or sub service is directly monetizable, which is where social graph analysis can help. The idea is to selectively choose which end pubs are considered potential good customers and filter out everyone else. If this needs to be said, I'll say it. This is not censorship. This is simply a business deciding whom to serve. Many different criteria can be used to select what is a good customer, especially since this definition depends on the service being provided.
A general approach is to use trust rank or spam mass estimates with a suitable measure for selecting the good seeds such as proof of work weight as the criteria for deciding the good customers. Those who don't qualify will simply not be served for free. User Level. Here we consider a simple defense against DoS attacks on user DMs. The user has a whitelist of end pubs that can directly DM him or her for free. If the sender is not on the white list, the DM will only be displayed by the client if it is preceded by or comes with a payment above some certain threshold.
The white list can be edited manually, but it's automatically populated based on some social graph criteria that the user has approved for example, all inpubs in some set can be automatically added to the white list of user v. Personalized ratings. The idea that every opinion or vote is equally important to everyone is a byproduct of political democracy and its sanctification. This is never the case in any social context. Imagine you want to buy a product that has thousands of good reviews but your friend recently bought it and tells you that it's not good.
It's natural for you to give more weight to your friend's opinion contrary to the belief that each opinion has equal weight. When computing an average there is always an assumption about the weight distribution for example, how important each vote is Social graph analysis can be useful to design more accurate and even personalized ratings. For example, ratings that reflect who the viewer is and what he or she is looking for at any given time. For example, you might want to give more weight to people who have similar interest to you or more weight to your friends and friends of friends many different criteria for calculating weight distributions are possible making this an exciting area for future research it's important to note that the most trustworthy ratings will be those that are independently verifiable both in the ratings and in weight distributions.
Practically speaking, there are 2 steps involved in computing a weight distribution select the support for example, the set of end pubs that have a non zero weight And 2, assigning a specific weight to each end pub that is part of the support. These two steps can be done simultaneously or one after the other. In the next section, we'll look at support selection. A problem that arises in another seemingly unrelated context, social discovery. Social discovery. How many times have you discovered something thanks to a friend's recommendation? This phenomenon can actually be productized to streamline the discovery of content, places, and people that are relevant to the end user. The goal is to construct another graph consisting of the recommendations for each of the users we want to serve.
This is a simplified scenario where what is being recommended are other nodes in the graph. Now, I need to pause here to tell you that this next section is essentially nothing but matrix math. Lots of equations. It's almost impossible to read in the format that I'm reading it to you now. So I'm going to essentially describe what it is that we're looking for here. I'm going to go down here to this one picture that has 3 different kinds of results of matrices. Right? And what what he's wanting to do is, under certain circumstances, who would you most likely follow or who is a good candidate to follow or who are you connected to that you might not know you're connected to? Like, for instance, there's a notion that he gives of direct propagation.
Like and let let's call the the vertices users. Right? So there's user 1, user 2, user 3, and they're connected in some way and the users that are the nodes, each one of us is a node. So if I'm a user and I'm connected to another user, say user 2, and that person is connected to user 3, but I'm not directly connected to user 3, I have a direct propagation over to user 3 through my relationship with user 2. There's another type called transposition and this one I really like. It's the notion that if there's 3 users and I'm connected to user 2 and a third user is also connected to user 2 and we're both following the same person. In the first example, I was following user 2 and user 2 was following user 3.
In this case both I, user 1, and user 3 are following user 2 So user 1, me, and user 3, who I have no direct connection with, are finding something that is of value in user 2. So therefore, we have kind of a connection and we can exploit not exploit that. We can use that, and it's called transposition. The first one is called direct propagation. The third one is very hard to describe. It's called co citation. So me, user 1, is directly following user 2 and user 3. User 4 is directly following only user 3 but because I am following user 3 and user 2 and user 4 is also following like I am user 3, then user 4 has some not ephemeral, but like let's call it an ethereal connection to user 2.
The assumption is because I'm following user 3 and I'm following user 2 and because user 4 is following user 3, then user 4 might also be interested in what user 2 has to say because I'm following the same person that user 4 is following. So even though user 2 isn't directly connected in that relationship, There is an ethereal connection simply because both me and user 4 are connected to user 3. So therefore, because I'm connected to user 2 and user 4 isn't user 4 may very well be interested in what user 2 is trying to say. By combining and experimenting with propagation rules and weighting criteria, it is possible to efficiently compute personalized, verifiable, and dynamic ratings for each user, making this an exciting area for future development.
Conclusion. This is a comprehensive yet superficial paper as it touches on many different areas without exhausting any. This was done intentionally to stimulate interest in the field of social graph analysis. More importantly, it aims to help the reader understand the significant impact this exciting new field can have on the web, although not without its risks. The Circle P is open for business and today's vendor is the Leathermint dotcom. That is the leathermint.com if you need a belt, if you need a passport holder, if you need a a a specifically a wallet, and some of the coolest wallets that there are, you will go to the leathermint.com, or you can find them on Noster at leathermint or on dead bird site formerly known as Twitter at the leathermint.
And if you use their coupon code and type in bitcoin and all one word, you'll get 10% off of your purchase, and they will return to me some satoshis for putting them on the air. Honestly, some of the best leather I've ever seen, some of the best craftsmanship I've ever seen, and as I always continuously harp, you cannot not not look at the stitching. If your stitching fails, the entire wallet fails. So if you want some quality, if you want sovereignty in your style, go to the leathermint.com. Okay. So I don't want to belabor this too much longer, but there's one particular thing in this piece that I I'm just kinda fascinated with. Now he kinda leans a little too hard on web of trust, the WOT scores as given in Coracle.
I think that those are fine. I I really do. I understand his arguments that suggest that it's not optimal. But just because something isn't optimal, doesn't necessarily mean that we need to throw the baby out with the bathwater. I like the web of trust idea. I think that we haven't seen it blossom yet. However, this particular gentleman's idea of proof of work keys, I cannot get out of my head. This makes sense to me. And I also think, honestly, in a very real way, I think that there's no reason that this couldn't work very well alongside web of trust or the w o t score in order for us to really filter out the garbage.
We need something to take out the trash. It doesn't matter what you're doing. If you're just if you're cooking dinner, you're going to generate trash. If you have old clothes at one point or another you're going to call those and you're going to have to throw them in the trash because you can't even donate them. They got like some of my t shirts have huge holes in them. I'm not going to donate that. That's ridiculous. You know, I use them outside mowing the lawn. But no matter what you do, you're gonna generate trash. You're going talk to a programmer and ask them about how they handle their trash in in coding, and they will have a solution that they use to handle garbage, whether it's what they do to keep their code clean or what the compiler does. It does something is going to do trash handling.
Right? And we have it in social media as well. And not just social media because Nostra's much more than that, as I've said before. But no matter what you do, there's garbage. And this this issue of of spinning up this public private key pair but it has to be mined I like this idea because this would stop the issue that I was telling you about before we began reading the piece. I'm getting bombarded. I could go to primal right now. I was there about an hour ago, and there's probably 27 new followers and only 3 of them are somebody that I might actually care about. 3 of them that I might consider human because they have, you know, a a different avatar than the avatar style that's coming down with these other 20, what, 24 followers. They all have these orange concentric rings that are like a clock, and it's they're all in a different confirmation, and it's really weird. And it's clearly not a human. I mean, so right there, I'm I'm I've got an algorithm in my head saying I don't want any of these guys. I want the 3 guys that I'm I'm pretty sure are human. Doesn't mean that they are. It just means that there's more of a chance that they're human.
But what would even be more of a chance than that is if they had real money to put on a line to build that private key. And say look I want a nostril key okay you get your first one for free and there's there's no way that you can really do what I'm suggesting here, but just bear with me. You get your first one for free. Everything else you gotta buy. You know, you you gotta you or or you could spin them up, but I'm I I've got a setting in my client that says unless you paid at least a dollar at least 2,000 satoshis. Unless you pay 2,000 satoshis for your proof of work key, which I would be able to tell by how much proof of work is tied and forever tied to your key, I I you you get filtered out of my feed I don't even see you you can follow me I'll never know it it doesn't matter and also, by the way, I might actually have a setting that I can automatically mute you. And that way, the web of trust could pick up, oh, you've muted this person. Doesn't count to your web of trust score. Oh, and by the way, huddlebond, if, I need to have a discussion with you a lot more a lot longer about web of trust and how that calculation works.
And what happens? Here's my question to hodelbod. What happens if I have 3,000 followers out of 12,000 followers that are obvious fakes and nobody else gives a shit about? Nobody else can even see them because they're only following these 17 people and they're not engaging with the community at all. Does that affect my web of trust? So that's my question directly to Huddlebot. I think that it kinda could, but I'm not sure, so I'll wait for the answer. But I don't believe for an instant that there's no way that Web of Trust doesn't have value. It does.
But I also believe that in conjunction with having to spend some satoshis, 1,000, 2,000, 10,000, how much should it cost? I don't know. I no idea whatsoever. That's not the point. The point is that for me to be able to spin up something on like a new account for something, I would have to actually do things and reach for stuff and and solve particular problems that we would like to believe that only a human could solve, to jump through at least a couple of hoops. I love the fact that it's easy to spin up a key on Noster, but I don't like the fact that, you know, a full one fifth to one quarter of my followers are clearly fake. They just are. I don't I'm not gonna like, at this point, I don't even wanna say, oh, I got 12,000 followers, so they're you know, I'm I'm badass. No.
Not when a quarter of them are obvious fakes. So we have issues. We're always gonna have these issues. Nothing is ever going to be solved because if everything was solvable, everything would already be solved. No new problems would crop up and we could just all go to our graves happy as a clam because there's no reason to actually live anymore. You see what I'm what I'm getting at? But these problems to me are fascinating and I hope you enjoyed this piece. Please go follow this Papelia guy. I'm hoping, that he has some more stuff about this coming on in the future. He has a very interesting way of publishing. It has something to do with, my favorite note taking app, Obsidian. I'm not exactly sure how he's doing it. Papalia, if you're listening, please answer my nostril note asking you the question exactly how you're leveraging Obsidian to make these notes or these, publications because it's honestly, I'm fascinated.
Can't get my head to not stop thinking about it, so get in touch with me. Tell me how you did it, and I will see you on the other side. This has been Bitcoin and and I'm your host, David Bennett. I hope you enjoyed today's episode and hope to see you again real soon. Have a great day.
Good morning. This is David Bennett, and this is Bitcoin and, a podcast where I try to find the edge effect between the worlds of Bitcoin, gaming, permaculture, podcasting, and education to gain a better understanding of all. Edge effect is a concept from ecology describing a greater diversity of life where the edges of 2 systems overlap. While species from either system can be found at the edge, it is important to note there are species in the overlap that exist in neither system, and that is what I seek to uncover. Uncover. So join me in discovering the variety of things being created as Bitcoin rubs up against other systems. It is 9:35 AM Pacific Daylight Time. It is the 18th day of June 2024, and this is episode 909 of Bitcoin. And today, I'm gonna do something completely different.
I'm going to bring you a reading of Papalia's Navigating the Social Graph. Now, I found this article, yesterday or the day before, and I kinda found it fascinating because it talks about things like web of trust and identity and denial of service attacks on your identity as somebody who's using Nostr, but it, you know, could be Facebook, it could be Twitter. This is like sort of the the social graph problem. And this piece goes into what is a social graph? And it's it's very good. However, there are there are some problems with it, not in its ideas, but in the fact that it is it's heavily weighted towards matricy math. There's some algebra.
There's using formulas to define certain terms, and I have done my best to be able to clip that out and not basically be sitting here saying a plus b equals, you know, c squared to the raised to the f sharp and all that stuff. So I've done my best to get that out of there with without destroying the central idea or the theme of this particular paper. Again, this paper is really good. And even if you don't know math, you should probably go read this paper. I will definitely be having the link to this paper in the show notes, and I highly recommend that you guys go read this because as we sail this novel river that is Nostra that we find ourselves on.
Who we follow and what types of recommendations we make it make for each other. And that includes, like, who should you follow? Should you follow somebody just because I'm following them? Or should you follow somebody until, like, wait to follow somebody until I tell you, hey, you probably should also be following this person. Is there inferences that can be drawn? If I follow somebody and you follow somebody, but we're not following each other, do we have something in common? Well, this paper is is bringing that all home. But one of the most interesting parts about this paper is when we start talking about the w o t or web of trust and proof of work whether it's proof of work that I actually wrote the note or proof of work as to whether or not I actually generated my own keys.
Like, because, like, right now I'm being spammed by followers and it it is the exact whoever wrote this bot for Noster, it's it's definitely the same person. Alright? It this is every day, I will get on to Noster, and at least 5 times during the day that I get on, I will notice that I have 27 new followers. But each one of these followers has almost the exact same avatar. It's like concentric orange circles that, more or less don't, you know, kinda don't line up, and they don't line up in certain patterns. It's sort of like think of, you know, the same clock face, but different pictures of the clock at different times. It's following only 17 people every time it follows somebody.
So every time one of these things one of the whenever this thing gets activated, it spins up a new insect and n pub key pair and it follows the exact same 17 people. So now I've got like, I don't know, it started happening when I was right around 8,000 followers, and now I'm up to almost 12,000 followers, and it's not fun because they don't mean anything to me. I don't I don't care about these things. They they they have all of them have 0 posts, of course. They only follow these certain people. They never actually do or engage with Noster or the general population of Noster in any way other than inflating our follower counts.
But what does that do to my web of trust? If something that nobody else constantly follow if if my web of trust is being diluted by a bunch of bots that don't make any sense or don't engage in any meaningful way. What does that what does that say about my account? Well, this paper goes through a lot of that stuff And I think we should probably be very, very attentive to how we start moving forward in the future because all of our trust is basically mitigated or provided by central parties, 3rd parties, to be exact, people like Elon Musk, people like Mark Zuckerberg, all these people. Right? They are providing some kind of nexus of which here's how you here's the way that you're going to interact with each other and here's the way you're going to discover each other and here's the way you're going to trust each other and I don't want any of that.
However when you're in a completely free and open environment that Nostra provides it can be a problem. We are experiencing different problems. And as we move through Noster and on into the future, how are we going to fix our little red wagon? Well, Papillia, if that is the way that I can pronounce that name, he's also known as the social graph guy. He has Noster, so I will be putting his in pub into the show notes hopefully he's gonna kind of clear some of this up or at least give us some things to think about so with no further ado here it is.
Navigating the Social Graph by Popela, the social graph guy. The emergence of decentralized social networks with sovereign identities, for example, Nostra, is a pivotal moment for the web, and it comes at the perfect time. In the age of artificial intelligence, where it's getting cheaper and cheaper to impersonate people, distort videos and images, or just make them up, how is it possible to know what's true? What does truth even mean these days? These are questions I will not attempt to answer. However, even if the ontology of truth has eluded philosophers forever, a more practical approach is needed if the web is not to degenerate into a nihilistic place where nothing is real, or worse, a place where everything requires identity verification and the approval of a central authority.
In this paper, you will find a definition of the social graph, principles for thinking about it, and practical ideas for using it for DOS prevention, social discovery, anti impersonation, accurate ratings, and more. You will also learn about alternative mechanisms that can be used in conjunction with social graph analysis to provide more compelling and complete solutions. The social graph. First, what is a graph? A graph is a mathematical structure consisting of a set of nodes or vertices and a set of edges edges are defined by a relation on a set For example, if node v and node u are related, then the pair is contained in a set.
The relationship can be symmetrical or asymmetrical, distinguishing between undirected and directed graphs. A social graph is simply a graph in which the nodes are human entities such as individuals, or companies, or groups. And the edges represent some form of social relationship such as being friends with, following, or being zapped. Before we go any further, we need to define another important notion. That of a neighborhood. A neighborhood is a set of vertices inside another set of vertices whose defining property is to be connected to a vertex in a set.
What problem is the social graph solving? The answer is many, in many different ways and in many different forms. We are talking about using it as a defense mechanism against impersonation, as a way to prevent DOS attacks, to facilitate social discovery and connections, and to calculate more accurate ratings. But before we dive into the implications, I ask you to pause for a moment to understand the magnitude of what we are discussing Principles. Don't fall into the trap of thinking that the engineers at YouTube were evil when they designed their algorithm. As the saying goes, the road to hell is paved with good intentions. If your implementation is successful, people's behavior will be affected.
That's why it's important to establish principles that can guide designers and developers in creating systems that are healthier than the world we're leaving behind. The principle of least interference. A good rule of thumb when designing systems that affect social dynamics is to follow the principle of least interference. The principle of least interference is this, solve the problem by implementing whatever mechanism has the least possible impact on social dynamics as a heuristic based on this principle it is generally better to focus on solving only one problem at a time since it is unlikely that solving multiple problems at once will not lead to a greater impact on those social dynamics.
A great example to analyze is the WOT score implemented by Coracle, which tries to reduce spam, imposters, and objectionable content all at once. Let's start by analyzing how it works as an anti impersonation mechanism. If you search for Lynn Alden, it is very likely, if you are part of a, quote, good network, that the in pub with the highest WOT is the real LIN, while all others are impostors. However, a simple warning icon above all other LINS would have accomplished the same goal without introducing a rating that can alter social dynamics. In fact, the WOT score represents the opinion of the crowd around you, which doesn't offer any nuance when comparing the reputation of different real people.
Crowd thinking is one of the biggest problems of the world that we are leaving, and it would be a silly exercise to bring it with us. Besides, we do not walk around with a number on our forehead in meat space, nor should we in cyberspace, even if that number depends on the observer. There is also another, more fundamental problem, which is that the user only has information about a limited subset of the network, as defined by the relation follow or mute. Therefore, if you set the WOT threshold above 0, you won't see anyone outside of that, which certainly prevents spam, but at the cost of discoverability. We'll come back to how to design a more effective anti impersonation system later.
Principle of relativity in cyberspace. The current web and our societies in general are built on an outdated paradigm that relies on and requires consensus about what is true, false, good, or bad. Unspeakable atrocities have been committed to achieve and maintain such consensus. However, we must move beyond this legacy of the past if we are to usher in a new era of freedom. The notion of a commonly shared belief or social truth is as dead in this new millennium as God was in the 19th century. The principle of relativity in cyberspace states, you cannot and should not dictate what is true or false, what is good content or bad content or spam for another person.
If you do, you will simply be replacing the beast with a new set of beliefs. It's true that there are many different clients to choose from, but there were many different web browsers back in the day and that didn't end well. The reality is that a global agreement on things has seldom been necessary. In fact, relative canonicality can be achieved without the need for a central authority. Wikipedia, a Nostril alternative to Wikipedia, as you have probably guessed, fully embraces this idea of relative canonicality. Each topic contains multiple entries.
There is no single source of truth that aims or claims to be neutral. Of course, this doesn't mean that all entities are equally good or useful. On the contrary, there is a very opinionated and biased way of sorting them, which depends on the observer. Regardless of the specific implementation, again, number on the forehead, this is the way to go. This is relativity in cyberspace. There is no global. The point is arguably a corollary of the previous principle. But it's so important that it needs to be fully explained. The notion of global is also dead.
Despite being the largest index in the world, Google only indexes a very small portion of the web. The engineering challenges behind indexing absolutely every person and every piece of content in an ever growing open and hostile decentralized network will likely exacerbate this outcome. Nostr will outgrow every index. As spammers become more sophisticated, crawlers will have to become more selective, limiting the percentage of the network that is indexed. Therefore, a client designer or developer, you really only have a limited view of what's happening. This reinforces the statement that you cannot decide for someone else what is good or bad. True or false?
You can't possibly know because what you have is a local limited view, just like everyone else's. Let's take Nosterband for example. As much as I appreciate their cool statistics, it's important to know their limitations. The version of trust rank that Nosterband implements, one, runs on the subset of the network that they are aware of. 2, uses an arbitrarily chosen set of NIP 0 5 providers for determining the so called good seeds. This approach is fine if it's used to defend the service provider against spammers. See DOS prevention section for improvements. However, such an arbitrary view, all are, but some less than others, shouldn't be imposed on the end user, but only used as a way for the service provider to select the customers it wants to serve.
Principle of natural patterns. I believe that technology is made for people, so it should serve us and improve our quality of life. We shouldn't adapt to technology. Technology should adapt to us. This context is no exception to the rule. Humans are social creatures and our social dynamics and behaviors have evolved through our long history. Therefore, before introducing new and artificial social dynamics, it's wiser to mimic the natural dynamics that have evolved with us for millennia. The principle of natural patterns states, when in doubt, try to mimic the social dynamics of meat space.
For example, in meat space, a friend of a friend is not a stranger, but it's not a friend either. It's something in between and a good candidate for a potential future friend. Another example is something that will hit home if you've ever been to a Bitcoin conference. When you're among people with the same interest, and I would argue mostly overlapping values, even if you've never met them, you can't say they're strangers. Examples abound, so there's really no reason to try to impose any artificial dynamics before exhausting what nature has given us.
To summarize, we take from this section 3 principles that will inform our analysis and design decisions moving forward. The principle of least interference, the principle of relativity in cyberspace, The Principle of Natural Patterns The Problems Finally, we can start discussing some concrete problems and ways to solve them. There are several well defined problems that social graph analysis can help with. Again, it's important to define them well if we want to follow the principle of least interference. The first problem we will analyze is protection against impersonation. One of the most important pieces to get right.
Anti impersonation, social endorsement. Who is the real Giacomo in this case? It's pretty simple. Pausing to describe a picture. 2 different Giacomo Zuco Twitter account banners. One states, has 68,000 followers, the other says 44. First of all, let's talk about what won't help us decide. The name, the profile picture, the bio, the date of creation. And no, not even the number of followers as this is a completely irrelevant metric in an open and hostile network. What helps us is the line below. Quote, followed by Bitcoin Africa Story, Nostril Wallet Connect, and 666 others you follow.
This is the signal. Follows can be faked, but follows from people you trust cannot be faked or are more expensive to get. This is a particularly good design because it adheres to the principle of relativity in cyberspace. It doesn't prescribe who the real Giacomo is like the aforementioned WOT score where every follower has the same weight but it shows you clues that you can decide to interpret in the way that you want the same idea can be applied to mutes which give you a few more clues. Obviously, such a system can be extended not only to the people you follow, but also to the people who are 2 hops away from you or any number of hops away from you.
However, the signal becomes increasingly diluted and unreliable the farther out you go. This approach is a good start but it can't really solve the impersonation problem on its own because it's limited in reach and suffers from a bootstrapping problem for new users which is really the same problem if you think about it There is also a subtle issue worth mentioning. What if a reputable person tries to impersonate someone else? This attack is possible because it won't be detected immediately. But the more prominent the person, the faster this attack will be detected causing the attacker to lose their hard earned reputation.
Furthermore, for truly prominent figures such as heads of state, this problem can be mitigated but not completely solved by third party credentials that can be displayed by clients. NIP 0 5 Providers. Another approach that has been tried is to rely on NIP 0 5 Providers. If I trust a NIP 0 5 provider to verify the people they serve then I can assign a non zero level of trust to their However, this approach obviously requires trust which isn't ideal since there are trustless approaches that provide better guarantees. Proof of work keys.
When we see a stranger enter the pub where we are, or when we are crowded on a subway, we find ourselves potentially vulnerable to strangers. In most civilized places, however, we don't live in a constant state of fear. And that comes from the fact that others are vulnerable as well. Everyone has skin in the game, even the aggressors, which means that the best way to play this game is not to mess with other people. On Noster however, the only consequence you can feel is the loss of your reputation, which by definition is worthless when the in pub is brand new however there is a way to add skin in the game for new keys For example, proof of work keys.
The process works like this. While creating your identity, your machine can generate a new private key until the corresponding public key, as a binary number, satisfies the following inequality the public key is less than or equal to some threshold, T, of your choice. Since there is no efficient way to do this, allegedly, this process requires the expenditure of computing power and energy. This adds a quantifiable and verifiable cost to any identity making credible impersonation expensive. However proof of work keys can't work at scale because 1, they require commitment at key creation which is a bad user experience because it reverses the try before you buy rule 2, the proof of work can't be updated over time.
And 3, it is not possible to delegate the work without delegating the key, supposedly. The last point implies that the economy of scale favors the attacker because they have multiple targets whereas the defender can only defend themself however there is a similar approach that offers what I believe are more appropriate trade offs proof of work endorsement Proof of work endorsement. A proof of work endorsement is a simple unsigned user metadata event containing a target in pub, a difficulty commitment threshold, t, and a nonce.
The nonce is randomly generated until the following condition is satisfied. Some event is less than or equal to 2 raised to the t power where the event is a cryptographic hash function such as sha256. If the condition is met, the proof of work endorsement is valid and the target npubs powwait becomes 256 minus t. What I am describing is basically NIP13, except that it should use kind 0 and it doesn't need a signature. What makes proof of work endorsements different from proof of work keys is that one, it doesn't require any commitment at key creation The proof of work can be updated over time.
It is possible to delegate the work without delegating the key. And 4, requires data availability because the work is separate from the key point 3 is what makes this approach feasible and scalable especially for mobile devices constrained by CPU and battery limitations. Point 4 isn't particularly bad because the events are small, public and the user has an incentive to store them. Example of how it works. Alice gives a n pub to the miner. Alice pays the miner $10 The miner then mines the proof of work endorsement with the target n pub.
The target n pub now has a non zero proof of work wait. How does it help? Well, if the target impub has a non zero proof of work weight, credible impersonation attempts require a cost. The attacker's social capital or proof of work weight becomes less and less valuable and is eventually lost as more people discover the attack and mute the attackers in pub. Furthermore, assuming that most of the proof of work weight is allocated to real people, it's possible to use its distribution to fix the shortcomings of social endorsement, for example.
A new user can be presented with the choice of following the inpubs with the highest trust rank where the good seeds are the inpubs with non zero proof of work wait. The same principle can be applied to an existing user trying to get information about the inpub that is many hops away from him or her. If you want to summarize this idea to the maximum, if the number of followers is meaningless that's not true for the number of followers with non zero proof of work weight I have started working on this idea and preliminary results are promising, but more thought is needed Colored Halo.
Another anti impersonation mechanism tried by Nostrudel is the use of a few characters of the public key in hex format to identify a unique color that is displayed around the user's profile picture. The idea is that if I am already following the real Lynn Alden, a credible impersonation attempt would require generating many times the key pair to get the same or very similar color. That requires energy which means cost. However, this information is only useful if I already follow the real Lynn and remember her color asking my trusted network for the color of a particular person is no different task than asking her in pub directly This means that this approach, while interesting, is not very useful.
Denial of Service Attack Prevention When analyzing denial of service attacks, we should distinguish between defense mechanisms that work at the network level, at the service provider level, and at the user level. Talking about the first case may seem to contradict the principle of relativity in cyberspace but here by network we mean any set of network participants who are willing to cooperate but not necessarily trust each other Network Level proof of work events basically apply the concept of HashCash not to email messages but to notes. There is not much to say here except to note that the proof of work can be outsourced to service providers that can most likely out compete the attackers inefficiency.
Service Provider Level. The best tool the service provider can use to prevent DOS' money. A paid service, either pay per use or subscription base, can work both for monetization and as a defense mechanism. However, not every service or sub service is directly monetizable, which is where social graph analysis can help. The idea is to selectively choose which end pubs are considered potential good customers and filter out everyone else. If this needs to be said, I'll say it. This is not censorship. This is simply a business deciding whom to serve. Many different criteria can be used to select what is a good customer, especially since this definition depends on the service being provided.
A general approach is to use trust rank or spam mass estimates with a suitable measure for selecting the good seeds such as proof of work weight as the criteria for deciding the good customers. Those who don't qualify will simply not be served for free. User Level. Here we consider a simple defense against DoS attacks on user DMs. The user has a whitelist of end pubs that can directly DM him or her for free. If the sender is not on the white list, the DM will only be displayed by the client if it is preceded by or comes with a payment above some certain threshold.
The white list can be edited manually, but it's automatically populated based on some social graph criteria that the user has approved for example, all inpubs in some set can be automatically added to the white list of user v. Personalized ratings. The idea that every opinion or vote is equally important to everyone is a byproduct of political democracy and its sanctification. This is never the case in any social context. Imagine you want to buy a product that has thousands of good reviews but your friend recently bought it and tells you that it's not good.
It's natural for you to give more weight to your friend's opinion contrary to the belief that each opinion has equal weight. When computing an average there is always an assumption about the weight distribution for example, how important each vote is Social graph analysis can be useful to design more accurate and even personalized ratings. For example, ratings that reflect who the viewer is and what he or she is looking for at any given time. For example, you might want to give more weight to people who have similar interest to you or more weight to your friends and friends of friends many different criteria for calculating weight distributions are possible making this an exciting area for future research it's important to note that the most trustworthy ratings will be those that are independently verifiable both in the ratings and in weight distributions.
Practically speaking, there are 2 steps involved in computing a weight distribution select the support for example, the set of end pubs that have a non zero weight And 2, assigning a specific weight to each end pub that is part of the support. These two steps can be done simultaneously or one after the other. In the next section, we'll look at support selection. A problem that arises in another seemingly unrelated context, social discovery. Social discovery. How many times have you discovered something thanks to a friend's recommendation? This phenomenon can actually be productized to streamline the discovery of content, places, and people that are relevant to the end user. The goal is to construct another graph consisting of the recommendations for each of the users we want to serve.
This is a simplified scenario where what is being recommended are other nodes in the graph. Now, I need to pause here to tell you that this next section is essentially nothing but matrix math. Lots of equations. It's almost impossible to read in the format that I'm reading it to you now. So I'm going to essentially describe what it is that we're looking for here. I'm going to go down here to this one picture that has 3 different kinds of results of matrices. Right? And what what he's wanting to do is, under certain circumstances, who would you most likely follow or who is a good candidate to follow or who are you connected to that you might not know you're connected to? Like, for instance, there's a notion that he gives of direct propagation.
Like and let let's call the the vertices users. Right? So there's user 1, user 2, user 3, and they're connected in some way and the users that are the nodes, each one of us is a node. So if I'm a user and I'm connected to another user, say user 2, and that person is connected to user 3, but I'm not directly connected to user 3, I have a direct propagation over to user 3 through my relationship with user 2. There's another type called transposition and this one I really like. It's the notion that if there's 3 users and I'm connected to user 2 and a third user is also connected to user 2 and we're both following the same person. In the first example, I was following user 2 and user 2 was following user 3.
In this case both I, user 1, and user 3 are following user 2 So user 1, me, and user 3, who I have no direct connection with, are finding something that is of value in user 2. So therefore, we have kind of a connection and we can exploit not exploit that. We can use that, and it's called transposition. The first one is called direct propagation. The third one is very hard to describe. It's called co citation. So me, user 1, is directly following user 2 and user 3. User 4 is directly following only user 3 but because I am following user 3 and user 2 and user 4 is also following like I am user 3, then user 4 has some not ephemeral, but like let's call it an ethereal connection to user 2.
The assumption is because I'm following user 3 and I'm following user 2 and because user 4 is following user 3, then user 4 might also be interested in what user 2 has to say because I'm following the same person that user 4 is following. So even though user 2 isn't directly connected in that relationship, There is an ethereal connection simply because both me and user 4 are connected to user 3. So therefore, because I'm connected to user 2 and user 4 isn't user 4 may very well be interested in what user 2 is trying to say. By combining and experimenting with propagation rules and weighting criteria, it is possible to efficiently compute personalized, verifiable, and dynamic ratings for each user, making this an exciting area for future development.
Conclusion. This is a comprehensive yet superficial paper as it touches on many different areas without exhausting any. This was done intentionally to stimulate interest in the field of social graph analysis. More importantly, it aims to help the reader understand the significant impact this exciting new field can have on the web, although not without its risks. The Circle P is open for business and today's vendor is the Leathermint dotcom. That is the leathermint.com if you need a belt, if you need a passport holder, if you need a a a specifically a wallet, and some of the coolest wallets that there are, you will go to the leathermint.com, or you can find them on Noster at leathermint or on dead bird site formerly known as Twitter at the leathermint.
And if you use their coupon code and type in bitcoin and all one word, you'll get 10% off of your purchase, and they will return to me some satoshis for putting them on the air. Honestly, some of the best leather I've ever seen, some of the best craftsmanship I've ever seen, and as I always continuously harp, you cannot not not look at the stitching. If your stitching fails, the entire wallet fails. So if you want some quality, if you want sovereignty in your style, go to the leathermint.com. Okay. So I don't want to belabor this too much longer, but there's one particular thing in this piece that I I'm just kinda fascinated with. Now he kinda leans a little too hard on web of trust, the WOT scores as given in Coracle.
I think that those are fine. I I really do. I understand his arguments that suggest that it's not optimal. But just because something isn't optimal, doesn't necessarily mean that we need to throw the baby out with the bathwater. I like the web of trust idea. I think that we haven't seen it blossom yet. However, this particular gentleman's idea of proof of work keys, I cannot get out of my head. This makes sense to me. And I also think, honestly, in a very real way, I think that there's no reason that this couldn't work very well alongside web of trust or the w o t score in order for us to really filter out the garbage.
We need something to take out the trash. It doesn't matter what you're doing. If you're just if you're cooking dinner, you're going to generate trash. If you have old clothes at one point or another you're going to call those and you're going to have to throw them in the trash because you can't even donate them. They got like some of my t shirts have huge holes in them. I'm not going to donate that. That's ridiculous. You know, I use them outside mowing the lawn. But no matter what you do, you're gonna generate trash. You're going talk to a programmer and ask them about how they handle their trash in in coding, and they will have a solution that they use to handle garbage, whether it's what they do to keep their code clean or what the compiler does. It does something is going to do trash handling.
Right? And we have it in social media as well. And not just social media because Nostra's much more than that, as I've said before. But no matter what you do, there's garbage. And this this issue of of spinning up this public private key pair but it has to be mined I like this idea because this would stop the issue that I was telling you about before we began reading the piece. I'm getting bombarded. I could go to primal right now. I was there about an hour ago, and there's probably 27 new followers and only 3 of them are somebody that I might actually care about. 3 of them that I might consider human because they have, you know, a a different avatar than the avatar style that's coming down with these other 20, what, 24 followers. They all have these orange concentric rings that are like a clock, and it's they're all in a different confirmation, and it's really weird. And it's clearly not a human. I mean, so right there, I'm I'm I've got an algorithm in my head saying I don't want any of these guys. I want the 3 guys that I'm I'm pretty sure are human. Doesn't mean that they are. It just means that there's more of a chance that they're human.
But what would even be more of a chance than that is if they had real money to put on a line to build that private key. And say look I want a nostril key okay you get your first one for free and there's there's no way that you can really do what I'm suggesting here, but just bear with me. You get your first one for free. Everything else you gotta buy. You know, you you gotta you or or you could spin them up, but I'm I I've got a setting in my client that says unless you paid at least a dollar at least 2,000 satoshis. Unless you pay 2,000 satoshis for your proof of work key, which I would be able to tell by how much proof of work is tied and forever tied to your key, I I you you get filtered out of my feed I don't even see you you can follow me I'll never know it it doesn't matter and also, by the way, I might actually have a setting that I can automatically mute you. And that way, the web of trust could pick up, oh, you've muted this person. Doesn't count to your web of trust score. Oh, and by the way, huddlebond, if, I need to have a discussion with you a lot more a lot longer about web of trust and how that calculation works.
And what happens? Here's my question to hodelbod. What happens if I have 3,000 followers out of 12,000 followers that are obvious fakes and nobody else gives a shit about? Nobody else can even see them because they're only following these 17 people and they're not engaging with the community at all. Does that affect my web of trust? So that's my question directly to Huddlebot. I think that it kinda could, but I'm not sure, so I'll wait for the answer. But I don't believe for an instant that there's no way that Web of Trust doesn't have value. It does.
But I also believe that in conjunction with having to spend some satoshis, 1,000, 2,000, 10,000, how much should it cost? I don't know. I no idea whatsoever. That's not the point. The point is that for me to be able to spin up something on like a new account for something, I would have to actually do things and reach for stuff and and solve particular problems that we would like to believe that only a human could solve, to jump through at least a couple of hoops. I love the fact that it's easy to spin up a key on Noster, but I don't like the fact that, you know, a full one fifth to one quarter of my followers are clearly fake. They just are. I don't I'm not gonna like, at this point, I don't even wanna say, oh, I got 12,000 followers, so they're you know, I'm I'm badass. No.
Not when a quarter of them are obvious fakes. So we have issues. We're always gonna have these issues. Nothing is ever going to be solved because if everything was solvable, everything would already be solved. No new problems would crop up and we could just all go to our graves happy as a clam because there's no reason to actually live anymore. You see what I'm what I'm getting at? But these problems to me are fascinating and I hope you enjoyed this piece. Please go follow this Papelia guy. I'm hoping, that he has some more stuff about this coming on in the future. He has a very interesting way of publishing. It has something to do with, my favorite note taking app, Obsidian. I'm not exactly sure how he's doing it. Papalia, if you're listening, please answer my nostril note asking you the question exactly how you're leveraging Obsidian to make these notes or these, publications because it's honestly, I'm fascinated.
Can't get my head to not stop thinking about it, so get in touch with me. Tell me how you did it, and I will see you on the other side. This has been Bitcoin and and I'm your host, David Bennett. I hope you enjoyed today's episode and hope to see you again real soon. Have a great day.
Reading of Papalia's Navigating the Social Graph
Web of Trust and Identity Issues
Navigating the Social Graph by Papalia
Anti-Impersonation and Social Endorsement
Proof of Work Endorsement
Denial of Service Attack Prevention
Personalized Ratings and Social Discovery
Conclusion and Future Implications
Reflections on Web of Trust and Proof of Work Keys
