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On 17/07/12 18:51, Tony Arcieri wrote:
> Now we actually do the collaborative filtering calculation: we look
> for peers that are similar to ourself, by inputting our own
> transfer history and the sparse matrix we calculated in the
> previous step.
> 
> The output should be peers similar to ourself: namely ones which 
> experience a similar history of success/failures and similar
> transfer rates. With enough information, this should begin to
> reveal this like which peers are "closest" to us on the network
> (i.e. least bottlenecked by the network relative to us, not
> geographical closeness or closeness in the DHT)
> 
> To be highly rated by such a function, the Sybil would have to
> pretend to be a normal peer long enough to establish behavioral
> data that might make it appear similar enough in its interactions
> that the collaborative filtering function might select it for use
> in the DHT or potentially storing files.

Thanks for the explanation. Could you say a bit more about what
information a peer publishes (and signs?) about itself and others?
What prevents a Sybil from claiming to have had the same interactions
as another peer, in order to gain that peer's trust?

Cheers,
Michael
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