Efficient verifiable privacy-preserving recommendation system

SONG Chun-zhi; DONG Xiao-lei; CAO Zhen-fu

doi:10.3969/j.issn.1000-5641.2018.02.005

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Mar. 2018

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SONG Chun-zhi, DONG Xiao-lei, CAO Zhen-fu. Efficient verifiable privacy-preserving recommendation system[J]. Journal of East China Normal University (Natural Sciences), 2018, (2): 41-51, 62. doi: 10.3969/j.issn.1000-5641.2018.02.005

Citation:

SONG Chun-zhi, DONG Xiao-lei, CAO Zhen-fu. Efficient verifiable privacy-preserving recommendation system[J]. Journal of East China Normal University (Natural Sciences), 2018, (2): 41-51, 62. doi: 10.3969/j.issn.1000-5641.2018.02.005

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Efficient verifiable privacy-preserving recommendation system

doi: 10.3969/j.issn.1000-5641.2018.02.005

School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China

Received Date: 2017-06-25
Publish Date: 2018-03-25

Abstract

Abstract

To address the problem of privacy disclosure in traditional personalized recommendation systems, this paper proposes an efficient verifiable privacy-preserving recommendation system, which can provide user the way to verify the correctness of the resulting model of cloud computing under the premise of protecting user's data privacy. This paper uses ridge regression to find the best-fit linear curve of user's input data, and implements Yao's garbled circuit to realize the computation and the correctness verification of the recommendation model. The user and the cloud use a newly-devised privacy preserving data aggregation method named AGG (Aggregation) to replace public key homomorphic encryption used in most existing work, which can reduce the computational overhead of the user and the cloud, thus making the system more efficient. The security analysis and the efficiency analysis of the scheme are given at the end of the article.
- personalized recommendation system,
- ridge regression,
- privacy preservation,
- garbled circuits,
- verifiable computation

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References(18)

References

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