Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (8): 387-395.doi: 10.11896/jsjkx.230800177

• Information Security • Previous Articles     Next Articles

Privacy-preserving Principal Component Analysis Based on Homomorphic Encryption

ZHANG Jindou, CHEN Jingwei, WU Wenyuan, FENG Yong   

  1. Chongqing School,University of Chinese Academy of Sciences,Chongqing 400714,China
  • Received:2023-08-28 Revised:2024-05-31 Online:2024-08-15 Published:2024-08-13
  • About author:ZHANG Jindou,born in 1997,postgra-duate.His main research interests include homomorphic encryption and information security.
    CHEN Jingwei,born in 1984,Ph.D,professor.His main research interests include error-free numerical computation and lattice-based cryptography.
  • Supported by:
    Ministry of Science and Technology Key Research and Development Programs(2020YFA0712300),Natural Science Foundation of Chongqing(cstc2021jcyj-msxmX0821,CSTB2023NSCQ-MSX0441,cstc2021yszx-jcyjX0004,2022YSZX-JCX0011CSTB,CSTB2023YSZX-JCX0008) and Western Light of the West Young Scholars program of the Chinese Academy of Sciences.

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Abstract: In real life,data is not interconnected between different industries,or even between different departments within the same industry.With the improvement of computer computing power,it is not computing power but data volume that restricts the effectiveness of model training.Therefore,in order to obtain a better algorithm model,relying solely on one party’s data is not enough.It needs the participation of two or more parties,which requires privacy protection for all parties.In addition,as data collection becomes more detailed,the data dimension also increases.For high dimension data,dimension reduction is an indispensable step.And in terms of dimension reduction,principal component analysis(PCA) is a commonly used method.Homomorphic encryption is a solution when two parties want to collaborate on privacy protection data dimension reduction.Homomorphic encryption can compute encrypted data while protecting data privacy,and can be used to compute the PCA on encrypted data.In this paper,a two party encrypted data PCA scheme is designed using the CKKS homomorphic encryption scheme and the power method for dominant eigenvectors,achieving the goal of dimension reduction while protecting the privacy of both parties’ data.By improving the traditional power method iteration steps,the expensive homomorphic ciphertext division is avoided,allowing for more iterations with small encryption parameters,thereby reducing the computing time and improving the accuracy of the computed results.Through testing on public datasets and comparing it with some existing schemes,the scheme reduces the computational time by about 80%,and reduces the mean squared error to within 1% compared to the plaintext computation results.

Key words: Homomorphic encryption, Privacy preserving, Principal component analysis, Singular value decomposition, Power method

CLC Number: 

  • TP309.7
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