Sybilguard: Defending against sybil attacks via social networks

Sybilguard: Defending against sybil attacks via social networks (2006)

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by Haifeng Yu , Michael Kaminsky , Phillip B. Gibbons , Abraham Flaxman

Venue:	In ACM SIGCOMM ’06
Citations:	126 - 5 self

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Datum	Value	Source
TITLE	Sybilguard: Defending against sybil attacks via social networks	INFERENCE
AUTHOR NAME	Haifeng Yu	SVM HeaderParse 0.2
AUTHOR AFFIL	Intel Research Pittsburgh Carnegie Mellon University	SVM HeaderParse 0.2
AUTHOR NAME	Michael Kaminsky	SVM HeaderParse 0.2
AUTHOR AFFIL	Intel Research Pittsburgh Carnegie Mellon University	SVM HeaderParse 0.2
AUTHOR NAME	Phillip B. Gibbons	SVM HeaderParse 0.2
AUTHOR AFFIL	Intel Research Pittsburgh Carnegie Mellon University	SVM HeaderParse 0.2
AUTHOR NAME	Abraham Flaxman	SVM HeaderParse 0.2
AUTHOR AFFIL	Categories and Subject Descriptors	SVM HeaderParse 0.2
ABSTRACT	Peer-to-peer and other decentralized, distributed systems are known to be particularly vulnerable to sybil attacks. In a sybil attack, a malicious user obtains multiple fake identities and pretends to be multiple, distinct nodes in the system. By controlling a large fraction of the nodes in the system, the malicious user is able to “out vote” the honest users in collaborative tasks such as Byzantine failure defenses. This paper presents SybilGuard, anovelprotocolfor limiting the corruptive influences of sybil attacks. Our protocol is based on the “social network ” among user identities, where an edge between two identities indicates a human-established trust relationship. Malicious users can create many identities but few trust relationships. Thus, there is a disproportionately-small “cut ” in the graph between the sybil nodes and the honest nodes. SybilGuard exploits this property to bound the number of identities a malicious user can create. We show the effectiveness of SybilGuard both analytically and experimentally.	SVM HeaderParse 0.2
YEAR	2006	INFERENCE
VENUE	In ACM SIGCOMM ’06	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	267--278	INFERENCE
CITATIONS	27 found	ParsCit 1.0