1. Improving Peer-to-Peer Networks “Limited Reputation Sharing in P2P Systems” “Robust Incentive Techniques for P2P Networks”

2. In this talk … Peer-to-Peer Networks –Properties / Challenges –Threats –Solutions

3. Peer-to-Peer Networks Large Populations High Turnover Asymmetry of interest Zero-cost identity

4. Peer-to-Peer Networks (2) Threats –Malicious users: share bad resources –Free-Riders: selfish users Solutions –“Limited Reputation Sharing in P2P Systems” by Sergio Marti and Hector Garcia-Molina –“Robust Incentive Techniques for Peer-to-Peer Networks” by M. Feldman, K. Lai, I. Stoica and J. Chuang

5. “Limited Reputation Sharing in P2P Systems” by Sergio Marti and Hector Garcia-Molina Studies the performance of P2P resource-sharing network in the presence of malicious nodes Uses limited information sharing between peers Studies the effect of reputation systems

6. System Model When a peer desires a file: –Queries all or a subset of the peers for the file –Collects responses (response set) –Selects a provider and accesses the resource –Verifies the authenticity of the file: V(R) –If V(R) determines that the file is invalid, the peer fetches another file from the response set Peer wants to minimize the use of V(R) “Limited Reputation Sharing in P2P Systems”

7. Threat Model: Malicious peers Propagate fake files Can pass false information Three types of malicious nodes: –N: Do not misinform –L: Lie about everyone –C: Collude and give bad ratings to good nodes and good ratings to bad nodes Subversion Set: the set of unique fake files π B : Percentage of malicious peers p B : Probability of a file to be in the Subversion set “Limited Reputation Sharing in P2P Systems”

8. Reputation Systems Peers collect statistics about other peers and form opinions about them (reputation rating) 0 <= Reputation Rating <= 1 Peers can share reputation ratings Selection procedure: selects from which peer to download based on reputation ratings “Limited Reputation Sharing in P2P Systems”

9. Reputation Systems (2) Random Selection: no reputation used Local Reputation System: uses only local reputation ratings –File selection: Select-Best – high load on few nodes Weighted – tries to distribute load –Friend-Cache (FC) with max size |FC| “Limited Reputation Sharing in P2P Systems”

10. Reputation Systems (3) Voting Reputation System: uses its own Reputation Rating R(q,r) and RRs of a quorum (Q) of its peers R(v,r). The combined rating is: –Quorum selection: Neighbor-voting Friend-voting Additional parameters: –  0 : default rating –  T : selection threshold Whitewashing “Limited Reputation Sharing in P2P Systems”

11. Metrics Efficiency –Total number of V(R) evaluations: –Verification ratio: Load –Load on node i: –Average load: Message Traffic: “Limited Reputation Sharing in P2P Systems”, r v  1

12. Metrics (2) Summary of Terms: “Limited Reputation Sharing in P2P Systems”

13. Simulation Fully connected power-law network –N = 1000 nodes –Max node degree d max = 40 –d avg = 3.1 –TTL = 5 Local reputation: 100 queries from a single node; 1 turnover per step; all malicious nodes change identity every 10 steps Voter Reputation: 100,000 queries; 1 turnover per 1000 steps; all malicious nodes change identity every 10,000 steps “Limited Reputation Sharing in P2P Systems”

14. Simulation (2) “Limited Reputation Sharing in P2P Systems”

15. Voting System Parameters “Limited Reputation Sharing in P2P Systems” Random: r v = 28.2 Local Reputation

16. Voting System Parameters (2) “Limited Reputation Sharing in P2P Systems” Malicious node type: N Friend voting

17. Voting System Parameters (3) “Limited Reputation Sharing in P2P Systems” Malicious node type: C Friend voting

18. Efficiency Comparisons (1) “Limited Reputation Sharing in P2P Systems” Malicious node type: N

19. Efficiency Comparisons (2) “Limited Reputation Sharing in P2P Systems” Malicious node type: N

20. Load on Good Nodes (1) “Limited Reputation Sharing in P2P Systems”

21. Load on Good Nodes (2) “Limited Reputation Sharing in P2P Systems”

22. Message Traffic “Limited Reputation Sharing in P2P Systems”

23. Conclusion Reputation rating decreases the times V(R) is performed Reputation sharing is less susceptible to malicious nodes White-Washing does have an impact Friend-Cache has to be big (around 100) to minimize Message Traffic. However, only the top few friends should be asked to vote Weighted and Best selection criteria have comparable efficiency, but Weighted selection distributes Load among nodes better “Limited Reputation Sharing in P2P Systems”

24. “Robust Incentive Techniques for Peer-to-Peer Networks” by M. Feldman, K. Lai, I. Stoica and J. Chuang Game Theoretic approach to address free-riding in P2P networks Design better incentives based on Generalized Prisoner’s Dilemma

25. Model Social Dilemma: Universal cooperation should maximize social welfare, but peers should benefit exploiting the cooperation of others Asymmetric Transactions: Should handle asymmetric payoff Untraceable defections: defections should be transparent Dynamic Population: Peers should enter and leave the system as they please and be able to change their strategy “Robust incentive Techniques for Peer-to-Peer Networks”

26. Generalized Prisoner’s Dilemma Client Always to cooperate (request service) Server chooses whether to Cooperate or Defect based on previous experience with the client Client cannot trace defections Agents alternate between client/server every game round Agents can exploit blind cooperation from others “Robust incentive Techniques for Peer-to-Peer Networks”

27. Population Dynamics In each round of the game, each player plays one game as client and one game as server At the end of each round, players can: –Mutate –Learn –Turnover –Stay the same “Robust incentive Techniques for Peer-to-Peer Networks”

28. Strategy A strategy consists of: –Decision function –Private or shared history –Server selection mechanism –Stranger policy Examples of Strategies: –100% Cooperate –100% Defect We want to strike a balance between the two: –Cooperate with good peers –Defect bad peers “Robust incentive Techniques for Peer-to-Peer Networks”

29. Decision Function For peer i: –p i is a measure of services it has provides –c i is a measure of services it has consumed i’s generosity: –Discriminates against peers that (for the time being) have consumed more than they have provided. Server will deny service even if it is of the same type Normalized generosity: –Servers treat peers with respect to their own generosity Reciprocative decision function = Cooperate with i with Pr. min(g j (i), 1) “Robust incentive Techniques for Peer-to-Peer Networks”

30. Simulation Server Selection, History and Stranger Policy are shown through simulation results “Robust incentive Techniques for Peer-to-Peer Networks”

31. Baseline Results “Robust incentive Techniques for Peer-to-Peer Networks”

32. Baseline Results (2) “Robust incentive Techniques for Peer-to-Peer Networks”

33. Server Selection Transactions are asymmetric –Most likely to receive service from peer that I gave service –Also likely to receive service from a peer that provided service earlier Store past servers and clients Randomly choose a server or a client for new transactions “Robust incentive Techniques for Peer-to-Peer Networks”

34. Shared History Every player knows about transactions that other players perform Increases the size of “known” servers to select from Advantage: Scales better with: –Large populations –High turnover –Asymmetry Drawback: –Susceptible to collusion –Harder to implement “Robust incentive Techniques for Peer-to-Peer Networks”

35. Simulation Results (Shared History & Selection) “Robust incentive Techniques for Peer-to-Peer Networks”

36. Simulation Results (Shared History & Selection) 2 “Robust incentive Techniques for Peer-to-Peer Networks”

37. Shared History Attack: Collusion Group of peers lie about transactions Positive (bad peer claims another bad peer cooperated) Negative (bad peer claims that good peer defected) Bad for objective (trust everyone) reputation Need subjective reputation mechanism “Robust incentive Techniques for Peer-to-Peer Networks”

38. Shared History Attack: Collusion (2) Can use Maxflow graph algorithm as a distributed, subjective reputation system Reputation i (j) = Services Received/Services Provided = “Robust incentive Techniques for Peer-to-Peer Networks”

39. Shared History Attack: Collusion (3) “Robust incentive Techniques for Peer-to-Peer Networks”

40. Shared History Attack: False Reports One node may want to lower the reputation of another Easily identified: one node says transaction occurred and the other says it didn’t Solution: Modify MaxFlow so that a node always believes nodes closer to it. “Robust incentive Techniques for Peer-to-Peer Networks”

41. Zero-cost identities Motivates whitewashing Problem: –a node generous to newcomers motivates whitewashing –A node stingy with newcomer discourages newcomers to join the network Solution: Stranger Adaptive Strategy –Cooperate with stranger with Pr. min(p s /c s, 1 ) Susceptible to traitor attack Solution: Bind p s + c s to a constant k and keep only ratio r=p s /c s “Robust incentive Techniques for Peer-to-Peer Networks”

42. Zero-cost identities (2) “Robust incentive Techniques for Peer-to-Peer Networks”

43. Zero-cost identities (3) “Robust incentive Techniques for Peer-to-Peer Networks”

44. Traitors “Robust incentive Techniques for Peer-to-Peer Networks”

45. Conclusion A strategy for P2P networks: –Model over Asymmetrical Generalized Prisoner’s Dilema –Decision function: Reciprocative function = Cooperate with i with Pr. min(g j (i), 1) –Shared (short term) history Use maxflow to rate peer reputation Adaptive Stranger policy –Discriminative selection mechanism “Robust incentive Techniques for Peer-to-Peer Networks”

46. Summing it all up … For a good P2P system we need: –Reputation rating (based on outcomes of past transactions) –Reputation sharing between peers –Subjective reputation (weighted function vs. maxflow) –Eliminate White-Washing or decrease its effectiveness –Short History