Canal: Scaling Social Network-Based Sybil Tolerance Schemes

by Bimal Viswanath , Alan Mislove , Mainack Mondal , Ansley Post , Krishna P. Gummadi

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Datum	Value	Source
TITLE	Canal: Scaling Social Network-Based Sybil Tolerance Schemes	SVM HeaderParse 0.2
AUTHOR NAME	Bimal Viswanath	SVM HeaderParse 0.2
AUTHOR AFFIL	MPI-SWS	SVM HeaderParse 0.2
AUTHOR NAME	Alan Mislove	SVM HeaderParse 0.2
AUTHOR AFFIL	Northeastern University	SVM HeaderParse 0.2
AUTHOR NAME	Mainack Mondal	SVM HeaderParse 0.2
AUTHOR AFFIL	MPI-SWS	SVM HeaderParse 0.2
AUTHOR NAME	Ansley Post	SVM HeaderParse 0.2
AUTHOR AFFIL	MPI-SWS	SVM HeaderParse 0.2
AUTHOR NAME	Krishna P. Gummadi	SVM HeaderParse 0.2
AUTHOR AFFIL	MPI-SWS	SVM HeaderParse 0.2
ABSTRACT	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.	SVM HeaderParse 0.2
CITATIONS	46 found	ParsCit 1.0