Wifi-reports: improving wireless network selection with collaboration
Proceedings of the 7th international conference on Mobile systems, applications, and services
Sybil-resilient online content voting
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
Collaborative scoring with dishonest participants
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
FaceTrust: assessing the credibility of online personas via social networks
HotSec'09 Proceedings of the 4th USENIX conference on Hot topics in security
Whanau: a sybil-proof distributed hash table
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Context based trust normalization in service-oriented environments
ATC'10 Proceedings of the 7th international conference on Autonomic and trusted computing
SybilLimit: a near-optimal social network defense against sybil attacks
IEEE/ACM Transactions on Networking (TON)
Temporal defenses for robust recommendations
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Defending against Sybil nodes in BitTorrent
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part II
Sybil defenses via social networks: a tutorial and survey
ACM SIGACT News
Aiding the detection of fake accounts in large scale social online services
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Sybil resilient identity distribution in P2P networks
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Iolaus: securing online content rating systems
Proceedings of the 22nd international conference on World Wide Web
Leveraging Social Feedback to Verify Online Identity Claims
ACM Transactions on the Web (TWEB)
Robust Sybil attack defense with information level in online Recommender Systems
Expert Systems with Applications: An International Journal
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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.