Nostr: How to overcome the digital dilemma of social graphs?

Open and interoperable content/social graphs lead to the formation of hegemony.

Metadata leakage

It is well known that Nostr’s NIP 04 direct messages leak metadata. This seems like an obvious flaw and has been pointed out multiple times. After all, how private is your communication if anyone can see who you are messaging, how frequently, at what time, the size of the messages, who else is mentioned, and link multiple different conversations to each other?

For those who “understand” Nostr (including myself), a common counterargument is “this is not a bug, it’s a feature”. This reminds me of the early days of the internet, where internet security was almost an afterthought and social platforms flourished on various anonymous confession-type applications. It was so much fun to be able to brag to friends about who you were having frequent private conversations with! Most conversations don’t actually need to be truly confidential, so let’s turn it into a game. The most important thing about Nostr is entertainment value.

However, metadata leakage is a problem. In some ways, Nostr’s direct messages are a big improvement over traditional private messages (the platform can no longer rat you out to the FBI), but they are also a huge step backwards (anyone can now rat you out to the FBI). I fully believe that we will be able to solve this problem with direct messages, but solving the problem with other data types inside Nostr may be more difficult.

Social content

For the past few months, I’ve been thinking about one use case for Nostr: a trust network for commenting and recommendations. The Sybil attacks that allow robots to threaten social networks have also been used by unethical sellers as marketing tools. Purchased reviews, platform collusion have destroyed the credibility of online product reviews, just as keyword stuffing has ruined Google search results. Proof of work is useless against this attack, because the problem is not one of quantity, but of false credibility. The correct tool to combat false credibility is a trust network – verifiable credibility related to the end user’s own social graph.

This is a huge opportunity for Nostr, and I’m very excited about it. Imagine you want to know if the Vibration Recomposition Impactor (VRSF) can give you noticeably toned abs in less than 6 days. There are over 4,000 five-star ratings on Amazon, with all the one-star ratings filled with typos and illogical statements. So it must work, and it will make you smarter! But unfortunately, the toned abs are actually an illusion given to you by “big gyms”. Now, imagine you can find three friends who have been scammed and ask them for their thoughts – you might get a lower average rating, and you’ll be more certain that the VRSF’s vibrating foam isn’t worth the investment.

This query can be applied to any product, service, or cultural experience. And you are not limited to asking for opinions from the entire social relationship graph. You can easily plan a list of foodies to help you choose a restaurant, or trusted bookworms to help you decide on the next book to read.

Currently, large tech companies are unable to do this because Facebook will not share its social graph with Google, and Google will not share its business data with Facebook. But if there is an open personnel and corporate database on Nostr, anyone can recombine these data islands in a novel and interesting way.


But let’s consider the drawbacks.

An open social graph plus recommendations means that you can not only ask your friends for their opinions on a given product, but also ask:

  • What a person’s friends think of the product
  • What kind of people like specific products
  • How products and personnel are aggregated

The last one is particularly interesting because it means that you can find reasonable answers to some interesting questions:

  • Does a certain region have a fertility problem?
  • What are the political tendencies of specific groups?
  • How effective are specific advertisements for specific groups?

This is a social experiment that has been heavily criticized in Facebook’s history. Democratizing this data does not prevent its relevance from becoming a violation of personal privacy, especially when conducting complex analyses that require a lot of computing resources, and the results of the analysis can be kept private. It needs to be made clear that this problem goes far beyond the combination of social information and public comments. This is just one example of many similar problems, and similar problems may arise in open databases of user behavior.

Frankly speaking, we may be able to hand over the panoramic prison of surveillance to possible overlords without reservation. Just as closed gardens used to manage and market by manipulating opinions or interests.

How to solve?

So what should we do? I hope there is a rating system based on my social graph, but not at the expense of sacrificing our collective privacy. When building Nostr to solve novel use cases, we need to keep this threat in mind. Zero-knowledge proof may be used here, or we can solve this problem by simply reconfiguring data storage. In the future, users can post information to a small number of relays they trust, which will not forward their data, similar to @fiatjaf’s NIP-29 chat proposal. Then, these relays can support more complex query interfaces so that too much information is not revealed when answering questions. One interesting thing about this approach is that it may push relays towards the PWN model used by BlueSky. Not all data needs to be processed in the same way, so we have flexibility in implementing these heuristic algorithms. Just like a note can be broadcast to everyone or sent to an individual or group, some comments or other activities may only be made public to people who have been verified in some way.

By: hodlbod


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