1. The EigenTrust Algorithm for Reputation Management in P2P Networks
Sepandar D.Kamvar, Mario T.Schlosser, Hector Garcia-Molina
Stanford University
Appeared in WWW 2003, Budapest, Hungary
Speaker : Yu-Hsin Shih
Date : 2007/09/04

2. Outline Problem Basic EigenTrust Distributed EigenTrust
Secure EigenTrust
Global Trust Value
Experiments and Results

3. Problem Motivation: Goal:
Reduce inauthentic files distributed by malicious peers in a P2P network
Goal:
Identify sources of inauthentic files and malicious peers against downloading from them

4. Problem (cont.) Problem
How to aggregate local trust value without a centralized server
From a few peers – cannot get a wide view of P2P network
From all peers – may congest the network

5. Problem (cont.)
Method
Each peer i is assigned a global trust value, which reflects the experiences of all peers in the network with peer i

6. Outline Problem Basic EigenTrust Distributed EigenTrust
Secure EigenTrust
Global Trust Value
Experiments and Results

7. Terminology Local trust value: cij Global trust value: ti
Peer 3
Local trust value: cij
The opinion that peer i has on peer j, based on past experience
Global trust value: ti
The trust that the entire system places on peer i
Peer 1
t3=0.55
t2=0.3
t1=0.15
t4=0
C21=0.3
C23=0.7
C14=0.01
C12=0.6
Peer 2
Peer 4

8. Basic EigenTrust Compute local trust value Normalize local trust value
Aggregate local trust value

9. Basic EigenTrust Compute local trust value Normalize local trust value
Aggregate local trust value

10. Compute Local Trust Value
Step 1: Peer i downloads a file from peer j
Positive transaction  tr(i,j) = 1
Negative transaction  tr(i,j) = -1
Step 2: Local trust value sij = sum of ratings of individual transactions that peer i has downloaded from peer j
sij = Σ tr(i,j)
eBay, yahoo

11. Basic EigenTrust Compute local trust value Normalize local trust value
Aggregate local trust value

12. Normalize Local Trust Value..
All cij are non-negative
ci1 + ci cin = 1
C14=0.1
C12=0.9
Peer 1
Peer 2
Peer 4
Malicious users may assign arbitrarily high local trust values to malicious peers and arbitrarily low values to good peers.

13. Normalize Local Trust Value (cont.)
Local trust vector ci :
contains all local trust values cij that peer i has on other peers j
Peer 1 c1
Peer 2
Peer 4
Peer 1
C12=0.9
Peer 2
C14=0.1
Peer 4

14. Normalize Local Trust Value (cont.)
Drawback
Normalized values do not distinguish between peers with no interaction and peers with poor experience
Normalized values are relative, not absolute interpretation

15. Basic EigenTrust Compute local trust value Normalize local trust value
Aggregate local trust value

16. Past History ?
Each peer biases its choice of downloads using its own opinion vector ci.
If it has had good past experience with peer j, it will be more likely to download from that peer.
Problem – Each peer has limited past experience. Knows few other peers.
Peer 4
Peer 1
Peer 6

17. Friends of Friends Ask for the opinions of people who you trust
Peer 4
Peer 1
Peer 2
Peer 8
Peer 6
Friends of Friends
Ask for the opinions of people who you trust
Weight their opinions by your trust in them

18. The Math t C T i What your friends think of peer k
Weight each friend’s opinion by how much you trust him.
Ask your friends j .1 .3 .2 .6 i T t C

19. Problems with Friends
Either you know a lot of friends, in which case you have to compute and store many values.
Or, you have few friends, in which case you won’t know many peers, even after asking your friends.

20. Goals Want each peer to Know all peers
Perform minimal computation and storage

21. To Know All Peers Ask your friends: t = CTci Ask friends’ friends:
Keep asking until t converges:
t = (CT)nci

22. Minimal Computation
Luckily, the global trust vector t, if is computed in this manner, converge to the same thing for every peer!
Therefore, each peer doesn’t have to store and computes its own global trust vector. The whole network can cooperate to store and compute t.

23. Non-distributed Algorithm
Initialize:
Repeat until converges

24. Practical Issues A priori notions of trust Inactive Peers
Malicious Collectives

25. A Priori Notions of Trust
Assuming that some peers in the network are trustworthy
Define a distribution p over pre-trusted peers P, such that pi = 1 / |P|
In the presence of malicious peers, t = (CT)n p will converge faster so p is used as the start vector

26. Inactive Peers
If peer i doesn’t download from anybody else or assigns 0 to all peers, cij will be undefined:
Redefine cij. If a peer i doesn’t know anyone or doesn’t trust anyone, he will choose to trust per-trusted peers
C - local trust value

27. Malicious Collectives
Break collectives by having each peer place at least some trust in the peers P that are not a part of the collective
How? Probabilistically, an agent crawling the network is less likely to get stuck crawling a malicious collective
Challenge: Make sure that no pre-trusted peer is a member of malicious collective

28. Outline Problem Basic EigenTrust Distributed EigenTrust
Secure EigenTrust
Global Trust Value
Experiments and Results

29. Distributed EigenTrust
Each peer i stores its local trust vector ci and global trust vector ti
Computation, storage, message overheads are minimal
In P2P networks, each peer has limited interactions with other peers. So most of the trust values will be 0.
In the case of a network with heavily active peers, limit the number of local trust values cij that each peer can report

30. Distributed EigenTrust Algorithm
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) = (1-a)(c1i t1(k) +…+
cni tn(k))+api
-Send your opinion cij and trust value ti(k+1) to your
acquaintances.
-Wait for the peers who know you to send you their trust
values and opinions. }

31. Algorithm Complexity
Algorithm converges fast: around 100 query cycles for a network of 1000 peers
Computed global trust values do not change significantly any more after a low number of iterations

32. Outline Problem Basic EigenTrust Distributed EigenTrust
Secure EigenTrust
Global Trust Value
Experiments and Results

33. Secure EigenTrust
Previously, each peer computes and reports its own global trust value ti.
Drawback: Malicious peers can easily report false trust values
Secure EigenTrust
A peer’s trust value must not be computed by and reside at the peer itself.
Trust value of one peer will be computed by more than one peer. (use majority vote to avoid malicious results)
Security is an important issue in P2P network!

34. Secure EigenTrust
M peers (score managers) compute trust value of peer i
Use Distributed Hash Table (DHT) to get M score mangers
Hash unique ID of peer (IP address, TCP port) using hash functions h1, h2, h3 into points in the logical coordinate space
The peers corresponding to the points become the score managers.
DHT 只是其中一種Secure的方法, 不是唯一。

35. Secure EigenTrust : Properties
Anonymity
A peer at a specific coordinate cannot find out for whom it computes the trust value
Randomization
Peers that enter the system cannot select at which coordinates in the hash space they want to be located
Redundancy
Several score managers compute the trust value for one peer

36. Outline Problem Basic EigenTrust Distributed EigenTrust
Secure EigenTrust
Global Trust Value
Experiments and Results

37. Global Trust Value When you get responses from multiple peers
Deterministic
Choose the one with highest trust value
Probabilistic
Choose a peer with probability proportional to its trust value

38. Deterministic Download
Choose the one with highest trust value
Advantage
High probability to access authentic files
Disadvantage
Highly trusted peers will be overloaded.
Newcomers have little chance to build up reputation

39. Deterministic Download
Choose the one with highest trust value

40. Probabilistic Download
Choose a peer with probability proportional to its trust value
Advantage
Limit number of unsatisfactory downloads
Balance network load
Allow newcomers to build up reputation

41. Probabilistic Download
Choose a peer with probability proportional to its trust value

42. Load Distribution Deterministic Download Choice
Probabilistic Download Choice

43. Using Global Trust Values
Isolating Malicious Peers
Select peers probabilistically based on trust value
, where d is a constant between 0 and 1.
In this way, a peer avoid downloading from another peer who gives it bad discriminatory service.

44. Using Global Trust Values
Stimulating Free-riders to Share
Reward reputable peers
Increase connectivity, greater bandwidth
Effect
Incentive to share files
Give non-malicious peers an incentive to delete inauthentic files

45. Outline Problem Basic EigenTrust Distributed EigenTrust
Secure EigenTrust
Global Trust Value
Experiments and Results

46. Threat Scenarios Malicious Individuals Malicious Collective
Always provide inauthentic files
Malicious Collective
Know each other. Give each other good opinions and give other bad opinions.

47. Threat Scenarios (cont.)
Camouflaged Collective
Provide authentic files some of the time to trick good peers into giving them good opinions.
Malicious Spies
Some members of the collective give good files all the time, but give good opinions to malicious peers.

48. Malicious Individuals

49. Malicious Collective

50. Camouflaged Collective

51. Malicious Spies

52. Summary
EigenTrust
Dramatically reduce number of inauthentic file downloads in the network
Robust to malicious peers
Low overhead

53. Reference Kamvar’s slides & paper

54. Thank you!