It turns out people have researched how to do algorithmic recommendations without users having to reveal their personal preferences, and I am intrigued. Apparently, in principle we could have the good parts of, say, Netflix suggesting more things you might want to watch, without exposing ourselves to entities like Facebook selling all our data.
See "Distributed Differential Privacy and Applications" by Narayan, for example. (Also that's the first CC-BY licensed PhD thesis I've seen!)
@jamey Definitely excited to read the paper, but my inutition has long told me that MinHash combined with an anonymising multicast network could be used for this purpose.
@alcinnz I wasn't familiar with MinHash, but after skimming Wikipedia, I think I see what you mean. Are you suggesting multicast so all participants see all hashes and can compute the result independently, or what? How would that go together?
@alcinnz Ah, yes, I keep looking at the IPFS PubSub stuff because it sure seems like that ought to be useful for something. 😅
The design goals and implementation details aren't well documented as far as I've found, but superficially it doesn't look like it provides any anonymity at present. At the least it looks like you can tell who the peers are in a group you've joined?
@jamey My understanding is that at the moment they use simple flood networking. So if you receive a message from someone you don't know if you're actually in the group or simply forwarding a message from it.
@alcinnz The way I interpreted the little bit they wrote about how it works, nodes only forward "floodsub" messages for groups they're actually in, so you can effectively only join groups that at least one of your peers participates in too. But I dunno!
@jamey Rereading it again, I think you're right.
If so, that could cause some flakiness with the recommendations engine I described. Oh, well. I'll figure it all out later.
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