Outsourced Similarity Search on Metric Data Assets

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  • Man Lung Yiu
  • ,
  • Ira Assent
  • Christian S. Jensen, Danmark
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.
OriginalsprogEngelsk
TidsskriftI E E E Transactions on Knowledge & Data Engineering
Vol/bind24
Nummer2
Sider (fra-til)338-352
Antal sider15
ISSN1041-4347
DOI
StatusUdgivet - 2012

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