An Efficient Cacheable Secure Scalar Product Protocol for Privacy-Preserving Data Mining

  • Duc H. Tran
  • Wee Keong Ng
  • Hoon Wei Lim
  • Hai-Long Nguyen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6862)


Computing scalar products amongst private vectors in a secure manner is a frequent operation in privacy-preserving data mining algorithms, especially when data is vertically partitioned on many parties. Existing secure scalar product protocols based on cryptography are costly, particularly when they are performed repeatedly in privacy-preserving data mining algorithms. To address this issue, we propose an efficient cacheable secure scalar product protocol called CSSP that is built upon a homomorphic multiplicative cryptosystem. CSSP allows one to reuse the already cached data and thus, it greatly reduces the running time of any privacy-preserving data mining algorithms that adopt it. We also conduct experiments on real-life datasets to show the efficiency of the protocol.


Secret Share Association Rule Mining Data Mining Algorithm Oblivious Transfer Minimum Support Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Duc H. Tran
    • 1
  • Wee Keong Ng
    • 1
  • Hoon Wei Lim
    • 1
  • Hai-Long Nguyen
    • 1
  1. 1.Nanyang Technological UniversitySingapore

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