NDLI: Privacy-preserving matching of spatial datasets with protection against background knowledge

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Cartography and information presentation: a graphics/visualization perspective

Privacy-preserving matching of spatial datasets with protection against background knowledge

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Privacy-preserving matching of spatial datasets with protection against background knowledge

Content Provider	ACM Digital Library
Author	Bertino, Elisa Vicente, Carmen Ruiz Shang, Ning Ghinita, Gabriel
Abstract	Private matching (or join) of spatial datasets is crucial for applications where distinct parties wish to share information about nearby geo-tagged data items. To protect each party's data, only joining pairs of points should be revealed, and no additional information about non-matching items should be disclosed. Previous research efforts focused on private matching for relational data, and rely either on space-embedding or on SMC techniques. Space-embedding transforms data points to hide their exact attribute values before matching is performed, whereas SMC protocols simulate complex digital circuits that evaluate the matching condition without revealing anything else other than the matching outcome. However, existing solutions have at least one of the following drawbacks: (i) they fail to protect against adversaries with background knowledge on data distribution, (ii) they compromise privacy by returning large amounts of false positives and (iii) they rely on complex and expensive SMC protocols. In this paper, we introduce a novel geometric transformation to perform private matching on spatial datasets. Our method is efficient and it is not vulnerable to background knowledge attacks. We consider two distance evaluation metrics in the transformed space, namely $L_{2}$ and L∞, and show how the metric used can control the trade-off between privacy and the amount of returned false positives. We provide an extensive experimental evaluation to validate the precision and efficiency of our approach.
Starting Page	3
Ending Page	12
Page Count	10
File Format	PDF
ISBN	9781450304283
DOI	10.1145/1869790.1869795
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-11-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Privacy-preserving data linkage Location privacy
Content Type	Text
Resource Type	Article

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