The Sybil attack plagues all peer-to-peer systems, and modern open distributed ledgers employ a number of tactics to prevent it from proof of work, or other resources such as space, stake or memory, to traditional admission control in permissioned settings. With SybilQuorum we propose an alternative approach to securing an open distributed ledger against Sybil attacks, and ensuring consensus amongst honest participants, leveraging social network based Sybil defences. We show how nodes expressing their trust relationships through the ledger can bootstrap and operate a value system, and general transaction system, and how Sybil attacks are thwarted. We empirically evaluate our system as a secure Federated Byzantine Agreement System, and extend the theory of those systems to do so.
Subsequent work questions a number of assumptions based on the analysis of real-world social graphs @cite_11 . This work is influential in that it highlights that the social graphs on which these defences rest, but truly capture trust judgments, and provide incentives for users to not accept any links, including to malicious nodes. In this work we also highlight a further limit of SybilInfer as originally proposed: it is an effective mechanism to detect Sybil regions in the presence of an attack, however it is also presenting a large number of false positives'' when the network is free of such attacks --- by misclasifying a large number of honest nodes as Sybils. We provide a solution to this problem.
@cite_11
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Many graphs in general, and social graphs in particular, are directed by nature. However, applications built on top of social networks, including Sybil defenses, information routing and dissemination, and anonymous communication require mutual relationships which produce undirected graphs. When undirected graphs are used as testing tools for these applications to bring insight on their usability and potential deployment, directed graphs are converted into undirected graphs by omitting edge directions or by augmenting graphs. Unfortunately, it is unclear how altering these graphs affects the quality of their mixing time. Motivated by the lack of prior work on this problem, we investigate mathematical tools for measuring the mixing time of directed social graphs and its associated error bounds. We use these tools to measure the mixing time of several benchmarking directed graphs and their undirected counterparts. We then measure how this difference impacts two applications built on top of social networks: a Sybil defense mechanism and an anonymous communication system.