yeah, mute lists, at least, some clients are putting encrypted blobs in the content field that contains lists of npubs inside it. #jumble lets you do this, there is two mute options, mute publicly and mute privately.
i don't think anyone has made private follow lists or any of the others, but all of them clients mostly disregard the content field unless they implement this feature, so it could be added to all of them.
it's not really the same because it's not discoverable though.
Amethyst has private follows (for users, hashtags, locations and communities) and private mutes. Most of the relay setting is save privately as well.
If you want to incorporate your encrypted mute or follow lists into Brainstorm so the data gets worked into WoT scores, we’ll need to figure out how to give Brainstorm access to this data. Several options. First: build a version of Brainstorm that you run locally with access to your signer so it can unencrypt the lists. Second: unencrypt the lists outside brainstorm, feed those lists into brainstorm which either you’re running (ideally) or someone else is running for you (less ideal but let’s be real), hope the lists don’t get leaked.
I was thinking we could do something similar to what federated learning does: part of the calculation is done on the client, part in the provider. With machine learning, the neural net is created by the client first and then sent to the server such that the server cannot really understand the raw data anymore.
That’s an idea worth exploring. Although I don’t think I understand how we would turn it into a ML problem, or how the raw data would be encoded in a way that the server can’t understand it. Probably I need to learn more about ML.
Or maybe in our case, the client just needs to finish the calculation that started on the server. 🤔 Heavier, but it could work
the only way i could envision this data being ok to publish is if it involved some kind of magic cryptographic mathematics to encode the data in such a way you can verify it without revealing very much of the graph itself, but enough that you can trust the modified weightings it creates. but honestly, it's beyond me, the hipster vibes around ZKP and privacy coins have kept me from being curious to learn, up to this point.
I have been reading about federated learning these days, there's very interesting possibilities.
i think that teh user's own private list might provide some parameters to add to the calculation, it won't be significant. there is also semantics to placing the follows in that place as well that are contrary to the principle of a web.
