Presentation Transcript

1. Fine-grained Private Matching for Proximity-based Mobile Social Networking

   Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University University of Tennessee Guanhua Yan Los Alamos National Laboratory INFOCOM 2012
2. Proximity-based Mobile Social Networking (PMSN) Social interaction Among physically proximate users Using mobile devices, e.g., smartphone or tablet Directly through the Bluetooth/WiFi interfaces Valuable complement to web-based online social networking Chat, file sharing, …
3. Private (Profile) Matching The process of two users comparing their profiles without disclosing any information beyond the comparison result An indispensible part of PMSN because People prefer to socialize with others having similar interests or background Privacy concern
4. Existing Private Matching Schemes User profile comprises a list of attributes chosen from an underlying attribute set Ex: interests [Li et al.’11], friends [Arb et al.’08], disease symptoms [Lu et al.’10]
5. Existing Private Matching Schemes Map private matching into the problem of Private set intersection (PSI), e.g., [Kissner&Song’05], [Ye et al.’08] Private set intersection cardinality (PSI-CA), e.g., [Freedman et al.’04], [Cristofaro& Tsudik’10] or
6. Limitations Cannot differentiate users with the same attribute Ex: suppose that Alice, Bob, and Mario all like movie ? Twice a week Watch movie twice a week Twice a month
7. Fine-grained Personal Profile
8. Fine-grained Private Matching Two users evaluate the similarity/distance between their personal profiles in a privacy-preserving fashion Finer differentiation Personalized profile matching Cannot be solved by PSI or PSI-CA
9. Outline System model, problem formulation and cryptographic tool Fine-grained private matching protocols Protocol 1 Protocol 2 Protocol 3 Protocol 4 Performance evaluation Conclusion
10. System Model Each user carries a mobile device, e.g., smartphone, with the same PMSN application installed Fine-grained profile Consists of attributes, e.g., interests User assigns an integer in to each attribute, e.g., to indicate the level of interest Each personal profile can be represented as a -dimensional vector
11. System Model (cont’) Take Alice and Bob as two exemplary users A PMSN session consists of three phases Bob Alice Neighbor discovery Profile matching Social interaction
12. Problem Formulation A set of candidate matching metrics Each is a function over two vectors measuring the distance between two personal profiles Alice chooses and runs a private matching protocol with Bob to compute
13. Privacy Levels Privacy-level 1 (PL-1) When protocols ends, Alice learns ; Bob learns Privacy-level 2 (PL-2) When protocols ends, Alice learns ; Bob learns nothing Privacy-level 3 (PL-3) When protocols ends, Alice learns if for some threshold of her choice; Bob learns nothing
14. Cryptographic Tools: Paillier Cryptosystem [Paillier’99] Encryption Homomorphic property Self-blinding property
15. Private Matching Protocol 1 (PL-1) A non-trivial adaption of [Rane et al. 2010] Matching metric: distance
16. Protocol Intuition For , define a function where We have Ex:
17. Protocol Intuition (cont’) Define We have
18. Protocol Intuition (cont’) We further have Known by Alice Dot product Known by Bob
19. Detailed Protocol Can be precomputed
20. Private Matching Protocol 2 (PL-2) Matching metric Any additively separable functions that can be written as , for some functions Ex: ( distance) (Dot product) (Weighted distance)
21. Protocol Intuition Convert any additive separable function into dot product computation For and , define functions and The th element is The th bit is1
22. Protocol Intuition (cont’) Let We have
23. Detailed Protocol Can be precomputed
24. Private Matching Protocol 3 (PL-3) Matching metric Any additive separable function When protocol ends, Alice learns if , Bob learns nothing
25. Protocol Intuition Let be three arbitrary positive integers, such that We have Assume that and are both integers The following inequalities are equivalent
26. Detailed Protocol Can be precomputed
27. Detailed Protocol (cont’)
28. Private Matching Protocol 4 (PL-3) Matching metric Protocols 1~3 cannot be directly applied Basic idea Transform into an additive function
29. Protocol Intuition: Similarity Matching
30. Protocol Intuition (cont’) Three properties of similarity score Additive separable Directly affected by the value of Related to according to the following theorem Protocol 4 can be realized as a special case of Protocol 3 by choosing the similarity score as matching metric
31. Performance Evaluation Compare Protocols 1~3 with RSV [Rane et al. 2010] 1024-bit exponentiation 1024-bit multiplication 2048-bit exponentiation 2048-bit multiplication
32. Simulation Results
33. Simulation Results
34. Conclusion We motivated the problem of fine-grained private matching for PMSN We presented a set of novel private matching protocols supporting different matching metrics and privacy levels
35. Thank you Q&A