NDLI: Private record matching using differential privacy

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Private record matching using differential privacy

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Private record matching using differential privacy

Content Provider	ACM Digital Library
Author	Bertino, Elisa Inan, Ali Kantarcioglu, Murat Ghinita, Gabriel
Abstract	Private matching between datasets owned by distinct parties is a challenging problem with several applications. Private matching allows two parties to identify the records that are close to each other according to some distance functions, such that no additional information other than the join result is disclosed to any party. Private matching can be solved securely and accurately using secure multi-party computation (SMC) techniques, but such an approach is prohibitively expensive in practice. Previous work proposed the release of sanitized versions of the sensitive datasets which allows blocking, i.e., filtering out sub-sets of records that cannot be part of the join result. This way, SMC is applied only to a small fraction of record pairs, reducing the matching cost to acceptable levels. The blocking step is essential for the privacy, accuracy and efficiency of matching. However, the state-of-the-art focuses on sanitization based on k-anonymity, which does not provide sufficient privacy. We propose an alternative design centered on differential privacy, a novel paradigm that provides strong privacy guarantees. The realization of the new model presents difficult challenges, such as the evaluation of distance-based matching conditions with the help of only a statistical queries interface. Specialized versions of data indexing structures (e.g., kd-trees) also need to be devised, in order to comply with differential privacy. Experiments conducted on the real-world Census-income dataset show that, although our methods provide strong privacy, their effectiveness in reducing matching cost is not far from that of k-anonymity based counterparts.
Starting Page	123
Ending Page	134
Page Count	12
File Format	PDF
ISBN	9781605589459
DOI	10.1145/1739041.1739059
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-03-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Differential privacy Privacy Security Record matching
Content Type	Text
Resource Type	Article

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