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Privacy preserving k-nearest neighbor classification over encrypted database in outsourced cloud environments

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Abstract

To utilize the cost-saving advantages of the cloud computing paradigm, individuals and enterprises increasingly resort to outsource their databases and data operations to cloud servers. However such solutions come with the risk of violating the privacy of users. To protect privacy, the outsourced databases are usually encrypted, making it difficult to run queries and other data mining tasks without decrypting the data first. Conventional encryption methods are either incapable of supporting such operations or computationally expensive to do so. In this paper, we aim to efficiently support computations over encrypted cloud databases, particularly focusing on privacy preserving k-nearest neighbor classification. The proposed scheme efficiently protects database security, key confidentiality of the data owner, query privacy and data access patterns. We analyze the cost of our proposed scheme and evaluate the performance through extensive experiments using both synthetic and real databases.

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Authors and Affiliations

  1. School of Electronic Science, National University of Defense Technology, Changsha, China

    Wei Wu, Jian Liu & Ming Xian

  2. School of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia

    Udaya Parampalli

Authors
  1. Wei Wu
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  2. Udaya Parampalli
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  3. Jian Liu
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  4. Ming Xian
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Correspondence to Wei Wu.

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Wu, W., Parampalli, U., Liu, J. et al. Privacy preserving k-nearest neighbor classification over encrypted database in outsourced cloud environments. World Wide Web 22, 101–123 (2019). https://doi.org/10.1007/s11280-018-0539-4

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  • DOI: https://doi.org/10.1007/s11280-018-0539-4

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