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Performance-Oriented Privacy-Preserving Data Integration

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Data Integration in the Life Sciences (DILS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3615))

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Abstract

Current solutions to integrating private data with public data have provided useful privacy metrics, such as relative information gain, that can be used to evaluate alternative approaches. Unfortunately, they have not addressed critical performance issues, especially when the public database is very large. The use of hashes and noise yields better performance than existing techniques, while still making it difficult for unauthorized entities to distinguish which data items truly exist in the private database. As we show here, the uncertainty introduced by collisions caused by hashing and the injection of noise can be leveraged to perform a privacy-preserving relational join operation between a massive public table and a relatively smaller private one.

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Author information

Authors and Affiliations

  1. UCLA Computer Science Department, Los Angeles, California, USA

    Raymond K. Pon

  2. Lawrence Livermore National Laboratory, Livermore, California, USA

    Terence Critchlow

Authors
  1. Raymond K. Pon
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  2. Terence Critchlow
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of California, Davis,  

    Bertram Ludäscher

  2. University of Maryland, College Park, 20742, MD, USA

    Louiqa Raschid

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© 2005 Springer-Verlag Berlin Heidelberg

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Pon, R.K., Critchlow, T. (2005). Performance-Oriented Privacy-Preserving Data Integration. In: Ludäscher, B., Raschid, L. (eds) Data Integration in the Life Sciences. DILS 2005. Lecture Notes in Computer Science(), vol 3615. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11530084_19

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  • DOI: https://doi.org/10.1007/11530084_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27967-9

  • Online ISBN: 978-3-540-31879-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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