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Privacy Preserving Inner Product of Vectors in Cloud Computing

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Listed:
  • Gang Sheng
  • Tao Wen
  • Quan Guo
  • Ying Yin

Abstract

The problem of privacy preserving inner product of vectors has been widely studied. Much work has been done on the scenario of two parties involved in the computation. In this paper, we consider the scenario where three parties are involved in the computing process of cloud computing. We propose a new privacy preserving scheme for inner product of two vectors in the cloud and give the correctness analysis and performance analysis for the scheme. The proposed scheme is based on homomorphic encryption, and the security can be guaranteed. Experiments show the efficiency of the scheme.

Suggested Citation

  • Gang Sheng & Tao Wen & Quan Guo & Ying Yin, 2014. "Privacy Preserving Inner Product of Vectors in Cloud Computing," International Journal of Distributed Sensor Networks, , vol. 10(5), pages 537252-5372, May.
    • Handle: RePEc:sae:intdis:v:10:y:2014:i:5:p:537252
    DOI: 10.1155/2014/537252
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