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    Enabling search services on outsourced private spatial data

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    Type
    Article
    Authors
    Yiu, Man Lung
    Ghinita, Gabriel
    Jensen, Christian Søndergaard
    Kalnis, Panos cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2009-10-30
    Online Publication Date
    2009-10-30
    Print Publication Date
    2010-06
    Permanent link to this record
    http://hdl.handle.net/10754/561477
    
    Metadata
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    Abstract
    Cloud computing services enable organizations and individuals to outsource the management of their data to a service provider in order to save on hardware investments and reduce maintenance costs. Only authorized users are allowed to access the data. Nobody else, including the service provider, should be able to view the data. For instance, a real-estate company that owns a large database of properties wants to allow its paying customers to query for houses according to location. On the other hand, the untrusted service provider should not be able to learn the property locations and, e. g., selling the information to a competitor. To tackle the problem, we propose to transform the location datasets before uploading them to the service provider. The paper develops a spatial transformation that re-distributes the locations in space, and it also proposes a cryptographic-based transformation. The data owner selects the transformation key and shares it with authorized users. Without the key, it is infeasible to reconstruct the original data points from the transformed points. The proposed transformations present distinct trade-offs between query efficiency and data confidentiality. In addition, we describe attack models for studying the security properties of the transformations. Empirical studies demonstrate that the proposed methods are efficient and applicable in practice. © 2009 Springer-Verlag.
    Citation
    Yiu, M. L., Ghinita, G., Jensen, C. S., & Kalnis, P. (2009). Enabling search services on outsourced private spatial data. The VLDB Journal, 19(3), 363–384. doi:10.1007/s00778-009-0169-7
    Publisher
    Springer Nature
    Journal
    The VLDB Journal
    DOI
    10.1007/s00778-009-0169-7
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00778-009-0169-7
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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