1
Fine-grained Private Matching for
Proximity-based Mobile Social
Networking
Rui Zhang, Yanchao Zhang
Arizona State University
Jinyuan (Stella) Sun
University of Tennessee
Guanhua Yan
Los Alamos National Laboratory
INFOCOM 2012
Proximity-based Mobile Social Networking
(PMSN)
2
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, …
Private (Profile) Matching
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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
Existing Private Matching Schemes
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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]
Existing Private Matching Schemes
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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
Limitations
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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
Fine-grained Personal Profile
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Movie
5
Movie
5
Sports
3
Sports
3
Cooking
0
Cooking
0
Movie
3
Sports
3
Cooking
0
Fine-grained Private Matching
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Two users evaluate the similarity/distance
between their personal profiles in a privacypreserving fashion
Finer
differentiation
Personalized profile matching
Cannot be solved by PSI or PSI-CA
Outline
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System model, problem formulation and
cryptographic tool
Fine-grained private matching protocols
Protocol
1
Protocol 2
Protocol 3
Protocol 4
Performance evaluation
Conclusion
System Model
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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
System Model (cont’)
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Take Alice and Bob as two exemplary users
A PMSN session consists of three phases
Alice
Neighbor discovery
Profile matching
Social interaction
Bob
Problem Formulation
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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
Privacy Levels
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Privacy-level 1 (PL-1)
When
protocols ends, Alice learns
Privacy-level 2 (PL-2)
When
protocols ends, Alice learns
nothing
; Bob learns
; Bob learns
Privacy-level 3 (PL-3)
When
protocols ends, Alice learns if
for
some threshold of her choice; Bob learns nothing
Cryptographic Tools: Paillier Cryptosystem
[Paillier’99]
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Encryption
Homomorphic property
Self-blinding property
Private Matching Protocol 1 (PL-1)
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A non-trivial adaption of [Rane et al. 2010]
Matching metric: distance
Protocol Intuition
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For
where
We have
Ex:
, define a function
Protocol Intuition (cont’)
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Define
We have
Protocol Intuition (cont’)
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We further have
Known by Alice Dot
product
Known by Bob
Detailed Protocol
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Can be precomputed
Private Matching Protocol 2 (PL-2)
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Matching metric
Any
additively separable functions that can be written
as
, for some functions
Ex:
( distance)
(Dot product)
(Weighted
distance)
Protocol Intuition
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Convert any additive separable function into dot
product computation
For
and , define functions and
The th element is
The th bit is1
Protocol Intuition (cont’)
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Let
We have
Detailed Protocol
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Can be precomputed
Private Matching Protocol 3 (PL-3)
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Matching metric
Any
additive separable function
When protocol ends, Alice learns if
learns nothing
, Bob
Protocol Intuition
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Let
be three arbitrary positive integers, such
that
We have
Assume that
and are both integers
The following inequalities are equivalent
Detailed Protocol
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Can be precomputed
Detailed Protocol (cont’)
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Private Matching Protocol 4 (PL-3)
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Matching metric
Protocols
1~3 cannot be directly applied
Basic idea
Transform
into an additive function
Protocol Intuition: Similarity Matching
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Protocol Intuition (cont’)
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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
Performance Evaluation
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Compare Protocols 1~3 with RSV [Rane et al. 2010]
Offline Comp.
Online Comp.
Comm. (bit)
RSV
Protocol 1
Protocol 2
Protocol 3
1024-bit exponentiation
1024-bit multiplication
2048-bit exponentiation
2048-bit multiplication
Simulation Results
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Simulation Results
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Conclusion
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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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Thank you
Q&A
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