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Fine-grained Private Matching for Proximity- based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University University of Tennessee Guanhua Yan Los Alamos National Laboratory 1
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Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Dec 17, 2015

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Page 1: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

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

INFOCOM 2012

Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University University of Tennessee

Guanhua YanLos Alamos National Laboratory

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Page 2: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

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

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Chat, file sharing, …

Page 3: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

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

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Page 4: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Existing Private Matching Schemes4

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]

Page 5: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Existing Private Matching Schemes5

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

Page 6: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Limitations6

Cannot differentiate users with the same attribute Ex: suppose that Alice, Bob, and Mario all like movie

Watch movie twice a week

Twice a week

Twice a month

?

Page 7: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Fine-grained Personal Profile7

Movie 5Sports 3Cooking 0

Movie 5Sports 3Cooking 0

Movie 3Sports 3Cooking 0

Page 8: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Fine-grained Private Matching8

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

Page 9: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Outline9

System model, problem formulation and cryptographic tool

Fine-grained private matching protocols Protocol 1 Protocol 2 Protocol 3 Protocol 4

Performance evaluation Conclusion

Page 10: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

System Model10

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

Page 11: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

System Model (cont’)11

Take Alice and Bob as two exemplary users A PMSN session consists of three phases

Neighbor discovery

Profile matching

Social interaction

BobAlice

Page 12: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Problem Formulation12

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

Page 13: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Privacy Levels13

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

Page 14: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Cryptographic Tools: Paillier Cryptosystem [Paillier’99]

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Encryption

Homomorphic property

Self-blinding property

Page 15: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Private Matching Protocol 1 (PL-1)15

A non-trivial adaption of [Rane et al. 2010]

Matching metric: distance

Page 16: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Protocol Intuition16

For , define a function where

Ex:

We have

Page 17: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Protocol Intuition (cont’)17

Define

We have

Page 18: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Protocol Intuition (cont’)18

We further have

Known by Alice Known by BobDot product

Page 19: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Detailed Protocol19

Can be precomputed

Page 20: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Private Matching Protocol 2 (PL-2)20

Matching metric Any additively separable functions that can be written

as , for some functions

Ex:

(Weighted distance)

( distance)(Dot product)

Page 21: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Protocol Intuition21

Convert any additive separable function into dot product computation

For and , define functions and

The th bit is1 The th element is

Page 22: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Protocol Intuition (cont’)22

Let

We have

Page 23: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Detailed Protocol23

Can be precomputed

Page 24: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Private Matching Protocol 3 (PL-3)24

Matching metric Any additive separable function

When protocol ends, Alice learns if , Bob learns nothing

Page 25: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Protocol Intuition25

Let be three arbitrary positive integers, such that

We have Assume that and are both integers The following inequalities are equivalent

Page 26: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Detailed Protocol26

Can be precomputed

Page 27: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Detailed Protocol (cont’)27

Page 28: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Private Matching Protocol 4 (PL-3)28

Matching metric

Protocols 1~3 cannot be directly applied Basic idea

Transform into an additive function

Page 29: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Protocol Intuition: Similarity Matching29

Page 30: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Protocol Intuition (cont’)30

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 3by choosing the similarity score as matching metric

Page 31: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Performance Evaluation

Compare Protocols 1~3 with RSV [Rane et al. 2010]

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Offline Comp.

Online Comp.

Comm. (bit)

RSV

Protocol 1

Protocol 2

Protocol 3 1024-bit exponentiation

2048-bit exponentiation

1024-bit multiplication

2048-bit multiplication

Page 32: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Simulation Results32

Page 33: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

Simulation Results33

Page 34: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

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

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Page 35: Fine-grained Private Matching for Proximity-based Mobile Social Networking INFOCOM 2012 Rui Zhang, Yanchao Zhang Jinyuan (Stella) Sun Arizona State University.

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Thank youQ&A