Efficient Fuzzy Matching and Intersection on Private Datasets

Ye, Qingsong; Steinfeld, Ron; Pieprzyk, Josef

doi:10.1007/978-3-642-14423-3_15

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…PSI techniques are limited to equality like matching functions, and extensions [12,41] allow for matching rules that require exact match on at least t out of T features. However these techniques achieve poor recall for general matching rules.…”

Section: Private Set Intersection (Psi)mentioning

confidence: 99%

Composing Differential Privacy and Secure Computation

Machanavajjhala

Flynn

et al. 2017

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

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Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: (1) perfect precision and high recall of matching pairs, (2) a proof of end-to-end privacy, and (3) communication and computational costs that scale subquadratically in the number of input records. We show that all of the existing solutions for PRL-including secure 2-party computation (S2PC), and their variants that use non-private or differentially private (DP) blocking to ensure subquadratic cost -violate at least one of the three desiderata. In particular, S2PC techniques guarantee end-to-end privacy but have either low recall or quadratic cost. In contrast, no end-to-end privacy guarantee has been formalized for solutions that achieve subquadratic cost. This is true even for solutions that compose DP and S2PC: DP does not permit the release of any exact information about the databases, while S2PC algorithms for PRL allow the release of matching records.In light of this deficiency, we propose a novel privacy model, called output constrained differential privacy, that shares the strong privacy protection of DP, but allows for the truthful release of the output of a certain function applied to the data. We apply this to PRL, and show that protocols satisfying this privacy model permit the disclosure of the true matching records, but their execution is insensitive to the presence or absence of a single non-matching record. We find that prior work that combine DP and S2PC techniques even fail to satisfy this end-to-end privacy model. Hence, we develop novel protocols that provably achieve this end-to-end privacy guarantee, together with the other two desiderata of PRL. Our empirical evaluation also shows that our protocols obtain high recall, scale near linearly in the size of the input databases and the output set of matching pairs, and have communication and computational costs that are at least 2 orders of magnitude smaller than S2PC baselines.

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Section: Private Set Intersection (Psi)mentioning

confidence: 99%

Composing Differential Privacy and Secure Computation

Machanavajjhala

Flynn

et al. 2017

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

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“…The protocol is secure under both HBC and malicious models. Recently, improving the efficiency of PSI protocols under malicious attacks becomes the main focus in this field .…”

Section: Related Workmentioning

confidence: 99%

Efficient privacy preserving matchmaking for mobile social networking

Wang

Hou

Xia

et al. 2014

Concurrency and Computation

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SUMMARYDiscovering new friends who share common attributes, named as matchmaking, is one of the most popular applications in mobile social networking. The key security issue in matchmaking is to protect users' personal information. In this paper, we propose a novel privacy preserving matchmaking framework, which can help users to find new friends in mobile social networking without leaking their privacy information. A user (called initiator) can find the one who shares maximum number of common attributes with him (called best match) among nearby users (called candidates), and only exchange an attribute intersection set with the best match, while other candidates only know the size of the attribute intersection set mutually. The analysis and simulation results show that our protocol can resist both semi-honest and malicious attacks while providing efficient matchmaking functionality.

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“…[22,29]) and specially fuzzy matching (see e.g. [18,9,31]) is a related area in that protocols for generic fuzzy matching can be viewed as approximate solutions for the above protocols given appropriate mappings that translate "close"enough fingerprints or locations to "similar"-enough strings. However, proposals in this area that focus on more generic distance functions that do not solve our specific problems efficiently since precise appropriate mappings that satisfy the above criterion are not easy to find.…”

Section: Related Workmentioning

confidence: 99%

Private Fingerprint Matching

Shahandashti¹,

Ogunbona

2012

Lecture Notes in Computer Science

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We propose a fully private fingerprint matching protocol that compares two fingerprints based on the most widely-used minutia-based fingerprint matching algorithm. The protocol enables two parties, each holding a private fingerprint, to find out if their fingerprints belong to the same individual. Unlike previous works, we do not make any simplifying assumption on the matching algorithm or use generic multiparty computation protocols in our constructions. We employ a commonly-used algorithm that works by first comparing minutia pairs from the two fingerprints based on their types, locations, and orientations, and then checking if the number of matching minutia pairs is more than a threshold, and we propose a concrete, scalable, and modular protocol. We prove security against honest-but-curious adversaries and discuss how security against malicious adversaries can be achieved using standard cryptographic techniques. Our protocol is realized using common cryptographic primitives and do not require pairing-or lattice-based cryptography.

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Efficient Fuzzy Matching and Intersection on Private Datasets

Cited by 9 publications

References 13 publications

Composing Differential Privacy and Secure Computation

Composing Differential Privacy and Secure Computation

Efficient privacy preserving matchmaking for mobile social networking

Private Fingerprint Matching

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