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The landmark hierarchy: a new hierarchy for routing in very large networks
SIGCOMM '88 Symposium proceedings on Communications architectures and protocols
A bridging model for parallel computation
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IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
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TrustDavis: A Non-Exploitable Online Reputation System
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P2P '06 Proceedings of the Sixth IEEE International Conference on Peer-to-Peer Computing
SybilGuard: defending against sybil attacks via social networks
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Experience with an object reputation system for peer-to-peer filesharing
NSDI'06 Proceedings of the 3rd conference on Networked Systems Design & Implementation - Volume 3
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Ostra: leveraging trust to thwart unwanted communication
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
Growth of the flickr social network
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SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Comparison of online social relations in volume vs interaction: a case study of cyworld
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Sybil-resilient online content voting
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
On the evolution of user interaction in Facebook
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A sketch-based distance oracle for web-scale graphs
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WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Sybil attacks against mobile users: friends and foes to the rescue
INFOCOM'10 Proceedings of the 29th conference on Information communications
An analysis of social network-based Sybil defenses
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Whanau: a sybil-proof distributed hash table
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Fast and accurate estimation of shortest paths in large graphs
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Understanding latent interactions in online social networks
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Measuring the mixing time of social graphs
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Bazaar: strengthening user reputations in online marketplaces
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Detecting and analyzing automated activity on twitter
PAM'11 Proceedings of the 12th international conference on Passive and active measurement
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Liquidity in credit networks: a little trust goes a long way
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Limiting large-scale crawls of social networking sites
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Dirty jobs: the role of freelance labor in web service abuse
SEC'11 Proceedings of the 20th USENIX conference on Security
Sybil defenses via social networks: a tutorial and survey
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Uncovering social network sybils in the wild
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Defending against large-scale crawls in online social networks
Proceedings of the 8th international conference on Emerging networking experiments and technologies
Iolaus: securing online content rating systems
Proceedings of the 22nd international conference on World Wide Web
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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. To address this problem, we first demonstrate that despite their differences, all proposed Sybil tolerance systems work by conducting payments over credit networks. These payments require max flow computations on a social network graph, and lead to poor scalability. We then present Canal, a system that uses landmark routing-based techniques to efficiently approximate credit payments over large networks. Through an evaluation on real-world data, we show that Canal provides up to a three-order-of-magnitude speedup while maintaining safety and accuracy, even when applied to social networks with millions of nodes and hundreds of millions of edges. Finally, we demonstrate that Canal can be easily plugged into existing Sybil tolerance schemes, enabling them to be deployed in an online fashion in real-world systems.