Privacy preserving schema and data matching

Monica Scannapieco, Ilya Figotin, Elisa Bertino, Ahmed K. Elmagarmid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

102 Citations (Scopus)

Abstract

In many business scenarios, record matching is performed across different data sources with the aim of identifying common information shared among these sources. However such need is often in contrast with privacy requirements concerning the data stored by the sources. In this paper, we propose a protocol for record matching that preserves privacy both at the data level and at the schema level. Specifically, if two sources need to identify their common data, by running the protocol they can compute the matching of their datasets without sharing their data in clear and only sharing the result of the matching. The protocol uses a third party, and maps records into a vector space in order to preserve their privacy. Experimental results show the efficiency of the matching protocol in terms of precision and recall as well as the good computational performance.

Original languageEnglish
Title of host publicationSIGMOD 2007
Subtitle of host publicationProceedings of the ACM SIGMOD International Conference on Management of Data
Pages653-664
Number of pages12
DOIs
Publication statusPublished - 30 Oct 2007
EventSIGMOD 2007: ACM SIGMOD International Conference on Management of Data - Beijing, China
Duration: 12 Jun 200714 Jun 2007

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Other

OtherSIGMOD 2007: ACM SIGMOD International Conference on Management of Data
CountryChina
CityBeijing
Period12/6/0714/6/07

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Keywords

  • Privacy
  • Record matching

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Scannapieco, M., Figotin, I., Bertino, E., & Elmagarmid, A. K. (2007). Privacy preserving schema and data matching. In SIGMOD 2007: Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 653-664). (Proceedings of the ACM SIGMOD International Conference on Management of Data). https://doi.org/10.1145/1247480.1247553