the eigentrust algorithm for reputation management in p2p networks
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The EigenTrust Algorithm for Reputation Management in P2P Networks
Sepandar D.Kamvar Mario T.Schlosser Hector Garcia-Molina
Presenter: Jianming Zhou
Problem
Problem in P2P: Inauthentic files distributed by malicious nodes
Objective: Identify the source of inauthentic files and bias
against downloading from them Basic Idea
Reputation System: Assign a trust value to each peer on its previous
behaviors
Challenges
Need an distributed reputation system Success like C/S reputation system (eg. eBay) Provide comprehensive evaluation of peer Lower overhead
Desired features Self-policy, i.e., no central server Maintain anonymity Robust to malicious node and sybil attack Minimal overhead
EigenTrust -- Intro
Basic idea Each peer has a Global Reputation given by the
local trust values assigned by other peers Terminology
Local trust value: cij
The opinion peer i has of peer j, based on past exp. Each time peer i downloads an authentic/inauthentic
file from peer j, cij increases/decreases.
Global trust value: ti The trust that the entire system places in peer i
More about Local trust value
Normalization Otherwise, malicious peers can assign arbitrarily
high local trust value to other malicious peers All cij is non-negative
ci1+ci2+ci3+…+cin = 1
Local Trust Vector: ci
contains all local trust values cij that peer i has of other peers j
Other Approaches
Foundation of EigenTrust Reply on past experience Friend-friend reference
Past Experience Each peer bias choice based on its vector ci Peer with good experience will likely be selected Problem: each peer has limited past experience, knowing
few other peers Friend-friend reference
Ask friend opinion of other peers Weight their opinion by your trust in them Problem: various number of friends for each peer
Ask friend j What their opinion of peer k
Weight your friend’s opinion by how much you trust them
EigenTrust
Combine and enhance Comprehensive, i.e., knowing all peers Lower overhead in term of computation and storage
Comprehensive Iterative friend-friend reference
Ask your friend: Ask their friend: Ask until all nodes:
N large, converge to same vector for every peer i Peers can cooperate to compute and store t
Basic Algorithm:non-distributed
Non-distributed Initialize
Repeat until converge
T(0)t
nn
1...
1
(k)T1)(k tCt
Distributed algorithm 1
Each peer store its local trust vector Each peer store its own global trust value Each peer compute its own ti
The component-wise version of
is the distribution over pre-trusted peers
Anti- malicious collectives and guarantee converge
Distributed algorithm 2
For each peer i { -First, ask peers who know you for their opinions of you. -Repeat until convergence { -Compute current trust value: ti
(k+1) = c1i t1(k) +…+ cni tn
(k) -Send your opinion cij and trust value ti
(k) to your acquaintances. -Wait for the peers who know you to send you their trust values and opinions. }}
Secure EigenTrust
Peer should not hold its own t Problem: malicious node can report false value Solution: different peer compute t for one peer
Leverage DHT
T should not be computed by only one peer Problem: malicious node can collude Solution: multiple score managers + majority
rule Score manager: peer i is j’s score manager if i
computes its global trust value
Usage of global trust value
Isolating malicious nodes bias the download from more reputable nodes Potential problem: highly trusted node
overloaded Incenting freeriders to share
Hinder the spread of inauthentic files
Performance
Setup Node selection in trust system
Deterministic algorithm => overload Probabilistic algorithm => balanced
Performance
Thread Model A:Individual Malicious Peers B:Malicious Collectives C:Malicious Collectives with Camoflouge D:Malicious Spies
Thread model A:
Thread model: B
Thread model: C
Thread model: D
Conclusion
EigenTrust Dramatically reduces number of inauthentic files
on the network. Robust to malicious peers. Low overhead
Q&A
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