Dissuasive Methods Against Cheaters in Distributed Systems

1. Dissuasive Methods Against Cheaters in Distributed Systems Kévin Huguenin Ph.D. defense, December 10th 2010 TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAAA

2. wireless losses fiber upon receive(x) y= x + y send y cable computer losses Distributed systems and Models Van Neuman crashes, bugs

3. bad pros cons Crashes, losses Dishonest users hardware rational people Fault models and Approaches Byzantine faults very bad

4. Approaches: example Preventing speed governing Masking more roads Dissuading speed traps & fines

5. Detection • Punishment Dissuasive Approach: How To

6. Human nature • Collaborative dissemination • Social nature • Computation in Social Networks Outline

7. Collaborative dissemination

8. Epidemics Collaborative DisseminationPrinciple

9. Receive 12, 22, 31 4 guests Propose 12, 22, 31 (free SMS) period 4x Request 12, 22 (free SMS) 4x Send 12, 22 (MMS) Collaborative DisseminationAttacks and Dissuasion 3x 3x Propose less Less guests Send less Bias selection

10. Verifications • Decision Did he? I contacted C A Did B send what I asked? Yes he did log B C No Ok C A F B E G H D Z Y Z Z Z Y Collaborative DisseminationChallenges and Solution score 0 punished     Propose less Less guests Send less Bias selection

11. Social networks

12. E.g., polling • “Should partners be invited?” No way! I have to prevent this from happening But what if people find out? No! What if my partner had to learn? Yes! But it sounds cheesy… Computation in Social Networks

13. A new model of entities • Reputation • Privacy • Computation • Set of entities • Input values • Compute ? Computation in Social Networks

14. Scalable and Secure distributed computations in Social networks The S3 problemDefinition

15. S3 candidatequadruple where is an arbitrary set, is a metric space and is a symmetric function The S3 problemDefinition: Candidate

16. -Scalabilitymessage, spatial and computational complexities are The S3 problemDefinition: Scalability

17. -Accuracywhere The S3 problemDefinition: Accuracy

18. Probabilistic anonymity For any trace generated from a non-trivial configuration For any coalition of faulty nodesFor any non-faulty node Exists another trace (generated from ) s.t. The S3 problemDefinition: Privacy

19. Privacy: probabilistic anonymity • Discard trivial input configurations • (strong): trivial = inputs can be inferred from output alone • (weak): trivial = all inputs are equal The S3problemDefinition: Privacy

20. Model of faulty-nodes: • Deviate from the protocol BUT • Never behave in such a way that their misbehavior is detected with certainty The S3 problemDefinition: faults

21. groups of size (ring) Solving (√,√,weak)-S3Architecture

22. -1 -1 -1 +2 +4 +4 +2 +4 +1 +1 +1 +1 +1 +4 -1 Solving (√,√,weak)-S3Demo: Polling

23. Theorem: • The protocol S3 computes aggregation functions for Solving (√,√,weak)-S3

24. Messages • Memory Solving (√,√,weak)-S3Proof: Scalability

25. Solving (√,√,weak)-S3Proof: Accuracy • Attack: Voting +1 +1 +1 +1 +1

26. Attack: Counting +1 -1  +1 -1 -1 +5 +1 +1 Solving (√,√,weak)-S3Proof: Accuracy

27. Solving (√,√,weak)-S3Proof: Accuracy • Attack: Token corruption

28. Impact of one faulty-node: • Voting: • Counting: • Aggregation along the ring: none • Relative error is Solving (√,√,weak)-S3Proof: Accuracy

29. Exists (w.h.p.) two equivalent traces where inputs are swapped Solving (√,√,weak)-S3Proof: Privacy Output unchanged p q Group with no faulty node

30. Can compute the multi-set of inputs • Can compute any regular function with a fixed input set Generalization

31. User-centric models practical solutions • Boundaries • Massively multi-player online games Conclusion & perspectives