Tech Reports | EECS at UC Berkeley

Jonathan David Traupman

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2007-75

May 24, 2007

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-75.pdf

This thesis investigates on-line reputation systems for peer-to-peer markets and presents a number of systems which increase robustness against attacks on individual reputations while still distinguishing between honest and dishonest user behavior. The fluidity of identity on-line and the unavailability of practical legal recourse make evaluating trust and risk in on-line markets both vital and difficult. Reputation systems have been proposed as one possible means of building trust among strangers by aggregating the experience of many users, and prove more-or-less effective in peer-to-peer marketplaces like eBay. However, the very attributes that make reputation systems helpful also make them a target for fraud. A good reputation is valuable, so some users may try to circumvent the system to gain a high reputation without effort. We look at two specific ways in which users attack reputation systems in peer-to-peer markets and discuss ways in which the damage can be mitigated. We first address retaliatory negative feedback, where a user leaves a negative feedback for someone who complained about their behavior. We show that allowing retaliation can result in a reputation system that is incapable of identifying low-quality users and allows cheating to go unpunished. We then present EM-Trust, a system that is better able to estimate true user quality even with high levels of retaliation. We next look at the issue of sybil attacks, where a single user creates a large collection of identities to increase his own reputation. We show that EigenTrust, a widely discussed algorithm that purports to resist similar collusion attacks, does not work against sybils. We then present Relative Rank, a transformation of EigenTrust that is both sybil resistant and better suited to peer-to-peer marketplaces. Finally, we discuss RAW, a variation of PageRank that offers additional guarantees of sybil-resistance. We demonstrate that it is possible to design reputation systems that are as effective as existing non-robust ones at discriminating between honest and dishonest user behavior, and considerably less affected by common attacks against these systems.

Advisors: Doug Tygar

BibTeX citation:

@phdthesis{Traupman:EECS-2007-75,
Author= {Traupman, Jonathan David},
Title= {Robust Reputations for Peer-to-peer Markets},
School= {EECS Department, University of California, Berkeley},
Year= {2007},
Month= {May},
Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-75.html},
Number= {UCB/EECS-2007-75},
Abstract= {This thesis investigates on-line reputation systems for peer-to-peer markets and presents a number of systems which increase robustness against attacks on individual reputations while still distinguishing between honest and dishonest user behavior.
The fluidity of identity on-line and the unavailability of practical legal recourse make evaluating trust and risk in on-line markets both vital and difficult. Reputation systems have been proposed as one possible means of building trust among strangers by aggregating the experience of many users, and prove more-or-less effective in peer-to-peer marketplaces like eBay. However, the very attributes that make reputation systems helpful also make them a target for fraud. A good reputation is valuable, so some users may try to circumvent the system to gain a high reputation without effort. We look at two specific ways in which users attack reputation systems in peer-to-peer markets and discuss ways in which the damage can be mitigated.
We first address retaliatory negative feedback, where a user leaves a negative feedback for someone who complained about their behavior. We show that allowing retaliation can result in a reputation system that is incapable of identifying low-quality users and allows cheating to go unpunished. We then present EM-Trust, a system that is better able to estimate true user quality even with high levels of retaliation.
We next look at the issue of sybil attacks, where a single user creates a large collection of identities to increase his own reputation. We show that EigenTrust, a widely discussed algorithm that purports to resist similar collusion attacks, does not work against sybils. We then present Relative Rank, a transformation of EigenTrust that is both sybil resistant and better suited to peer-to-peer marketplaces. Finally, we discuss RAW, a variation of PageRank that offers additional guarantees of sybil-resistance.
We demonstrate that it is possible to design reputation systems that are as effective as existing non-robust ones at discriminating between honest and dishonest user behavior, and considerably less affected by common attacks against these systems.},
}

EndNote citation:

%0 Thesis
%A Traupman, Jonathan David 
%T Robust Reputations for Peer-to-peer Markets
%I EECS Department, University of California, Berkeley
%D 2007
%8 May 24
%@ UCB/EECS-2007-75
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-75.html
%F Traupman:EECS-2007-75