MuON: Epidemic Based Mutual Anonymity

1. MuON: Epidemic Based Mutual Anonymity Neelesh Bansod, Ashish Malgi, Byung Choi and Jean Mayo

2. Introduction: P2P Networks • Peer to peer networks • Peers cooperate to achieve each others goals • Peers contribute and share resources • Focus on unstructured P2P networks • Completely distributed

3. Introduction: P2P Networks • Characteristics of P2P networks • Large sizes • Untrusted participants • End-hosts cannot be trusted • Churn • Continual changes in system membership/topology • Rate of change (churn) varies with applications • Challenges for anonymous communication • Latency, reliability, resource consumption and anonymity need to scale with increasing churn and size.

4. Anonymity • Anonymity • Identities on Internet are not hidden • Hide the participant identities and communication • Type of anonymity guarantees • Initiator anonymity • Responder anonymity • Mutual anonymity

5. Anonymity Approaches • Anonymity by random path forwarding I A B C D R • Message loss • Current node leaves the network • Increasing churn causes increasing losses

6. Anonymity Approaches • Anonymity by creating routes I A B C D R • Creates anonymous routing to forward messages • Node on path leaves the network • Anonymous path breaks • Path needs to be detected and reconstructed • Increasing churn causes increasing losses

7. Anonymity Approaches • Anonymity by group communication I D A C B R • Suitable for networks with churn • Needs multicasting primitive • Scalable, reliable and bounded latency • Large groups • high overhead • high anonymity

8. MuON: Basic idea • Public key is used to identify recipient • Small header created for each message • Header is encrypted using public key of recipient • Message and header have suitable encryption, checksums and nonce for confidentiality and integrity. • Non-encrypted field in header called owner • IP of node (or owner) with message

9. MuON: Basic idea • Header is multicast to all members • Use an epidemic protocol for multicasting • Reliable delivery to all members • Bounded latency in large groups • Larger message sent to subgroup • Dynamically created subgroups • For a given header, a peer pulls the corresponding message from the owner with probability Pinter intermediate probability • Node becomes the new owner • If a node can decrypt the header, it pulls the message • Reliable delivery at recipient

10. HDR_X HDR_X MuON: Basic idea B R A X C

11. HDR_X HDR_X MuON: Basic idea B R A X C

12. HDR_A HDR_A HDR_X HDR_X MuON: Basic idea B R A X C

13. HDR_X HDR_A HDR_A HDR_C HDR_C MuON: Basic idea B R A X C

14. HDR_B HDR_B HDR_B HDR_X HDR_X HDR_C MuON: Basic idea B R A X C

15. HDR_C HDR_A HDR_B MuON: Basic idea B R A X C

16. MuON: Reliability

17. MuON: Latency

18. MuON: Bandwidth consumption

19. MuON: Anonymity Pinter= 0.3 Pinter= 0.1 Pinter= 0.5 Pinter= 0.8

20. Conclusion • Churn in P2P provides interesting challenges • Epidemic protocols provide a solution • MuON provides reliable anonymous communication with interesting properties • Future work • Further improve anonymity of MuON • Cannot withstand global adversary • Investigate use in different applications like service availability

21. Thank You Questions ??

22. MuON: Message format • Sending message from I to S • MSG={r1, id, data}ksession • Data encrypted with ksession for confidentiality • Contains nonce r1 and identifier for integrity • Profile (hdr)={r1,ksession, kI+,{H(D)} kI-} kS+ • D={r1,ksession, kI+,MSG} • Message identifier is H(hdr)

23. Anonymity Guarantees

24. Simulation Model of P2P • r: Single run of protocol (150 above) • d: Network churn • Average session time: 1 + 10(1-d)(r -1)

25. Reliability

26. Reliability

27. Latency

28. Latency

29. Header overhead

30. Header overhead

31. Data overhead

32. Data overhead