Abstract—Recently, there has been significant research interest in leveraging social networks to defend against Sybil attacks. While much of this work may appear similar at first glance, existing social network-based Sybil defense schemes can be divided into two categories: Sybil detection and Sybil tolerance. These two categories of systems both leverage global properties of the underlying social graph, but they rely on different assumptions and provide different guarantees: Sybil detection schemes are application-independent and rely only on the graph structure to identify Sybil identities, while Sybil tolerance schemes rely on application-specific information and leverage the graph structure and transaction history to bound the leverage an attacker can gain from using multiple identities. In this paper, we take a closer look at the design goals, models, assumptions, guarantees, and limitations of both categories of social network-based Sybil defense systems. I.

There has been a flurry of research on leveraging social networks to defend against multiple identity, or Sybil, attacks. A series of recent works does not try to explicitly identify Sybil identities and, instead, bounds the impact that Sybil identities can have. We call these approaches Sybil tolerance; they have shown to be effective in applications including reputation systems, spam protection, online auctions, and content rating systems. All of these approaches use a social network as a credit network, rendering multiple identities ineffective to an attacker without a commensurate increase in social links to honest users (which are assumed to be hard to obtain). Unfortunately, a hurdle to practical adoption is that Sybil tolerance relies on computationally expensive network analysis, thereby limiting widespread deployment.

cooperative systems by