SybilRank

Proof of Humanity in Social Graph.

Aiding the Detection of Fake Accounts in Large Scale Social Online Services

• We introduce a new tool in the hands of OSN operators, which we call SybilRank . It relies on social graph properties to rank users according to their perceived likelihood of being fake (Sybils). SybilRank is computationally efficient and can scale to graphs with hundreds of millions of nodes, as demonstrated by our Hadoop prototype. We deployed SybilRank in Tuenti’s operation center. We found that ∼90% of the 200K accounts that SybilRank designated as most likely to be fake, actually warranted suspension. On the other hand, with Tuenti’s current user-report-based approach only ∼5% of the inspected accounts are indeed fake.

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• https://www.usenix.org/system/files/conference/nsdi12/nsdi12-final42_2.pdf
• (DeepL) We present a new tool, which we call SybilRank, in the hands of OSN operators. This tool uses properties of the social graph to rank users according to their likelihood of being imposters (Sybils).SybilRank is computationally efficient and scalable to graphs with hundreds of millions of nodes, as demonstrated in our Hadoop prototype. We deployed SybilRank in our Tuenti operations center. We found that of the 200,000 accounts that SybilRank identified as likely fakes, about 90% actually had to be suspended. In contrast, Tuenti's current user-report-based approach found that only about 5% of the accounts inspected were truly fake.

from Omoikane Study Group

• (World ID) cites "the unique Proof of Humanity of the individual" as a "key identity-based challenge."
• For example, SybilRank.

  • SybilRank is computationally efficient... Scalable to graphs with hundreds of millions of nodes

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  • "Trust Seeds", an algorithm that propagates trust from highly trusted accounts.
  • How can "accounts that correspond to retinal scans" be "Trust Seeds" with a high degree of trust?

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