SybilControl: practical sybil defense with computational puzzles

  • Authors:
  • Frank Li;Prateek Mittal;Matthew Caesar;Nikita Borisov

  • Affiliations:
  • Massachusetts Institute of Technology, Cambridge, MA, USA;University of California Berkeley, Berkeley, CA, USA;University of Illinois Urbana-Champaign, Urbana, IL, USA;University of Illinois Urbana-Champaign, Urbana, IL, USA

  • Venue:
  • Proceedings of the seventh ACM workshop on Scalable trusted computing
  • Year:
  • 2012

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Abstract

Many distributed systems are subject to the Sybil attack, where an adversary subverts system operation by emulating the behavior of multiple distinct nodes. Most recent works addressing this problem leverage social networks to establish trust relationships between users. However, social networks are not appropriate in all systems. They can be subverted by social engineering techniques, require nodes to maintain and be aware of social network information, and may require overly optimistic assumptions about the fast-mixing nature of social links. This paper explores an alternate approach. We present SybilControl, a novel decentralized scheme for controlling the extent of Sybil attacks. It is an admission and retainment control scheme for nodes in a distributed system that requires them to periodically solve computational puzzles. SybilControl consists of a distributed protocol to allow nodes to collectively verify the computational work of other nodes, and mechanisms to prevent the malicious influence of misbehaving nodes that do not perform the computational work. We investigate the practical issues involved with deploying SybilControl into existing DHTs, particularly with handling churn. SybilControl is shown to provide strict bounds on the size of Sybil attacks, given adversaries with finite resources. We also show through simulations that the performance overhead of enabling SybilControl is manageable using commonplace DHT churn-handling techniques. This provides strong evidence that SybilControl can be practically deployed.