A fast secure dot product protocol with application to privacy preserving association rule mining

Changyu Dong, Liqun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

18 Citations (Scopus)
75 Downloads (Pure)

Abstract

Data mining often causes privacy concerns. To ease the concerns, various privacy preserving data mining techniques have been proposed. However, those techniques are often too computationally intensive to be deployed in practice. Efficiency becomes a major challenge in privacy preserving data mining. In this paper we present an efficient secure dot product protocol and show its application in privacy preserving association rule mining, one of the most widely used data mining techniques. The protocol is orders of magnitude faster than previous protocols because it employs mostly cheap cryptographic operations, e.g. hashing and modular multiplication. The performance has been further improved by parallelization. We implemented the protocol and tested the performance. The test result shows that on moderate commodity hardware, the dot product of two vectors of size 1 million can be computed within 1 minute. As a comparison, the currently most widely used protocol needs about 1 hour and 23 minutes.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part I
EditorsVincent S. Tseng, Tu Bao Ho , Zhi-Hua Zhou, Arbee L. P. Chen, Hung-Yu Kao
PublisherSpringer
Pages606-617
Number of pages12
ISBN (Print)9783319066073
DOIs
Publication statusPublished - 2014

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8443
ISSN (Print)0302-9743

Keywords

  • data mining
  • knowledge discovery
  • artificial intelligence
  • information storage

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  • Cite this

    Dong, C., & Chen, L. (2014). A fast secure dot product protocol with application to privacy preserving association rule mining. In V. S. Tseng, T. B. Ho , Z-H. Zhou, A. L. P. Chen, & H-Y. Kao (Eds.), Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part I (pp. 606-617). (Lecture Notes in Computer Science; Vol. 8443). Springer. https://doi.org/10.1007/978-3-319-06608-0_50