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.
 The Internet is an amazingly powerful tool for connecting people together, unmatched in human history. Yet, with that power comes great potential for spam and abuse. Trust metrics are an attempt to compute the set of which people are trustworthy and which are likely attackers. This chapter presents two specific trust metrics developed and deployed on the Advogato Website, which is a community blog for free software developers. This real-world experience demonstrates that the trust metrics fulfilled their goals, but that for good results, it is important to match the assumptions of the abstract trust metric computation to the real-world implementation. Distributed Hash Tables (DHTs) are very efficient distributed systems for routing, but at the same time vulnerable to disruptive nodes. Designers of such systems want them used in open networks, where an adversary can perform a sybil attack by introducing a large number of corrupt nodes in the network, considerably degrading its performance. We introduce a routing strategy that alleviates some of the effects of such an attack by making sure that lookups are performed using a diverse set of nodes. This ensures that at least some of the nodes queried are good, and hence the search makes forward progress. This strategy makes use of latent social information present in the introduction graph of the network.