Outsourced similarity search on metric data assets

Man Lung Yiu, Ira Assent, Christian S. Jensen, Panos Kalnis

Research output: Journal article publicationJournal articleAcademic researchpeer-review

57 Citations (Scopus)

Abstract

This paper considers a cloud computing setting in which similarity querying of metric data is outsourced to a service provider. The data is to be revealed only to trusted users, not to the service provider or anyone else. Users query the server for the most similar data objects to a query example. Outsourcing offers the data owner scalability and a low-initial investment. The need for privacy may be due to the data being sensitive (e.g., in medicine), valuable (e.g., in astronomy), or otherwise confidential. Given this setting, the paper presents techniques that transform the data prior to supplying it to the service provider for similarity queries on the transformed data. Our techniques provide interesting trade-offs between query cost and accuracy. They are then further extended to offer an intuitive privacy guarantee. Empirical studies with real data demonstrate that the techniques are capable of offering privacy while enabling efficient and accurate processing of similarity queries.
Original languageEnglish
Article number5620912
Pages (from-to)338-352
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume24
Issue number2
DOIs
Publication statusPublished - 2 Jan 2012

Keywords

  • and protection
  • integrity
  • Query processing
  • Security

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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