Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Scalable Sweeping-Based Spatial Join
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Properties of Embedding Methods for Similarity Searching in Metric Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information sharing across private databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Privacy preserving schema and data matching
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Private queries in location based services: anonymizers are not necessary
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A Hybrid Approach to Private Record Linkage
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient Private Record Linkage
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Secure kNN computation on encrypted databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Privacy-preserving set operations
CRYPTO'05 Proceedings of the 25th annual international conference on Advances in Cryptology
Location privacy protection in the presence of users' preferences
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Location privacy attacks based on distance and density information
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
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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 L2 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.