DSybil: Optimal Sybil-Resistance for Recommendation Systems

Recommendation systems can be attacked in various ways, and the ultimate attack form is reached with a {\em sybil attack}, where the attacker creates a potentially unlimited number of {\em sybil identities} to vote. Defending against sybil attacks is often quite challenging, and the nature of recommendation systems makes it even harder. This paper presents {\em DSybil}, a novel defense for diminishing the influence of sybil identities in recommendation systems. DSybil provides strong provable guarantees that hold even under the worst-case attack and are optimal. DSybil can defend against an unlimited number of sybil identities over time. DSybil achieves its strong guarantees by i) exploiting the heavy-tail distribution of the typical voting behavior of the honest identities, and ii) carefully identifying whether the system is already getting ``enough help'' from the (weighted) voters already taken into account or whether more ``help'' is needed. Our evaluation shows that DSybil would continue to provide high-quality recommendations even when a million-node botnet uses an optimal strategy to launch a sybil attack.