A Multi-party Vertically Partitioned Data Synthesis Mechanism with Personalized Differential Privacy

ZHU Youwen; WANG Ke; ZHOU Yuqian

doi:10.11999/JEIT231158

Volume 46 Issue 5

May 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(5): 2159-2176

ZHU Youwen, WANG Ke, ZHOU Yuqian. A Multi-party Vertically Partitioned Data Synthesis Mechanism with Personalized Differential Privacy[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2159-2176. doi: 10.11999/JEIT231158

Citation:

ZHU Youwen, WANG Ke, ZHOU Yuqian. A Multi-party Vertically Partitioned Data Synthesis Mechanism with Personalized Differential Privacy[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2159-2176. doi: 10.11999/JEIT231158

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A Multi-party Vertically Partitioned Data Synthesis Mechanism with Personalized Differential Privacy

doi: 10.11999/JEIT231158

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Funds: The National Key Research and Development Program of China (2021YFB3100400), The National Natural Science Foundation of China (62172216), The Natural Science Foundation of Jiangsu Province (BK20211180)

Received Date: 2023-10-25
Rev Recd Date: 2024-04-17

Available Online: 2024-05-07

Publish Date: 2024-05-30

Abstract

Abstract

In today’s era, with the rapid development of big data technology and the continuous increase in data volume, large amounts of data are constantly collected by different companies or institutions, aggregating and publishing data owned by different companies or institutions helps to better provide services and support decision-making. However, their respective data may contain privacy information with different degrees of sensitivity, thus personalized privacy protection requirements need to be met while aggregating and publishing data from all parties. To solve the problem of multi-party data publication while ensuring that different privacy protection needs of all parties are met, a Multi-party Vertically partitioned Data Synthesis mechanism with Personalized Differential Privacy (PDP-MVDS) is proposed. Low-dimensional marginal distributions are firstly generated to reduce the dimension of high-dimensional data, then a randomly initialized dataset with these marginal distributions are updated, and finally a synthesized dataset whose distribution is similar to that of the real aggregated dataset from all parties is published. Personalized differential privacy protection is achieved by dividing the privacy budget; Secure scalar product protocol and threshold Paillier encryption algorithm are used to ensure the privacy of each party’s data in the aggregation process; Distributed Laplace perturbation mechanism is used to effectively protect the privacy of marginal distributions that aggregated from those parties. Through rigorous theoretical analysis, it is proved that PDP-MVDS can ensure the security of each participant’s data and the finally published dataset. Furthermore, the experimental results on public datasets show that PDP-MVDS mechanism can obtain a multi-party synthesized dataset with high utility under low overhead.
- Privacy protection,
- Multi-party data publication,
- Secure Multi-Party Computation(SMPC),
- Personalized Differential Privacy(PDP),
- Vertically partitioned data

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References(25)

References

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