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    Outsourced similarity search on metric data assets

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    Articlefile1.pdf
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    Type
    Article
    Authors
    Yiu, Man Lung
    Assent, Ira
    Jensen, Christian S.
    Kalnis,Panos
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2012-02
    Permanent link to this record
    http://hdl.handle.net/10754/562069
    
    Metadata
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    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.
    Citation
    Yiu, M. L., Assent, I., Jensen, C. S., & Kalnis, P. (2012). Outsourced Similarity Search on Metric Data Assets. IEEE Transactions on Knowledge and Data Engineering, 24(2), 338–352. doi:10.1109/tkde.2010.222
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Knowledge and Data Engineering
    DOI
    10.1109/TKDE.2010.222
    Additional Links
    http://ieeexplore.ieee.org/document/5620912/
    http://www4.comp.polyu.edu.hk/%7Ecsmlyiu/journal/TKDE_metricpriv.pdf
    ae974a485f413a2113503eed53cd6c53
    10.1109/TKDE.2010.222
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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