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Multi-party Security Computation with Differential Privacy over Outsourced Data

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10656))

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Abstract

Differential privacy has received considerable attention for privacy-preserving machine learning applications. In particular, in the cloud computing environment, data are outsourced from different users. Processing outsourced computations on the joint distribution of multi-party’s data under multiple public keys with differential privacy is a significant and difficult problem. In this paper, we propose a scheme named 1, multi-party security computation with differential privacy over outsourced data (\(\mathtt {MSCD}\)) by using a combination of public-key encryption with a double decryption algorithm (DD-PKE) and \(\epsilon \)-differential privacy to solve this problem. In our work, the cloud server adds the corresponding different statistical noises according to different queries of the data analyst, which differs from previous works in which noise is added by the data provider. In the random oracle model, our scheme is proven to achieve the goal of outsourced computation on the data sets of multiple parties without privacy leakage.

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Acknowledgments

This work was supported by Natural Science Foundation of Guangdong Province for Distinguished Young Scholars (2014A030306020), Guangzhou scholars project for universities of Guangzhou (No. 1201561613), Science and Technology Planning Project of Guangdong Province, China (2015B010129015), National Natural Science Foundation of China (Nos. 61472091, 61702126) and National Natural Science Foundation for Outstanding Youth Foundation (No. 61722203).

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Authors and Affiliations

  1. School of Computer Science and Educational Software, Guangzhou University, Guangzhou, 510006, China

    Ping Li & Jin Li

  2. Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, 3 Shangyuancun, Beijing, 100044, China

    Heng Ye

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  1. Ping Li
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Correspondence to Jin Li .

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Editors and Affiliations

  1. Guangzhou University , Guangzhou, China

    Guojun Wang

  2. Edith Kinney Gaylord Presidential Professor, University of Oklahoma, Norman, Oklahoma, USA

    Mohammed Atiquzzaman

  3. Aalto University, Espoo, Finland

    Zheng Yan

  4. University of Texas at San Antonio, San Antonio, Texas, USA

    Kim-Kwang Raymond Choo

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Li, P., Ye, H., Li, J. (2017). Multi-party Security Computation with Differential Privacy over Outsourced Data. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10656. Springer, Cham. https://doi.org/10.1007/978-3-319-72389-1_39

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  • DOI: https://doi.org/10.1007/978-3-319-72389-1_39

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