Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy

KANG Haiyan; JI Yuanrui; ZHANG Shuxuan

doi:10.1049/cje.2021.00.274

Volume 31 Issue 4

Jul. 2022

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Article Navigation > Chinese Journal of Electronics > 2022 > 31(4): 741-751

KANG Haiyan, JI Yuanrui, ZHANG Shuxuan, “Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy,” Chinese Journal of Electronics, vol. 31, no. 4, pp. 741-751, 2022, doi: 10.1049/cje.2021.00.274

Citation:

KANG Haiyan, JI Yuanrui, ZHANG Shuxuan, “Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy,” Chinese Journal of Electronics, vol. 31, no. 4, pp. 741-751, 2022, doi: 10.1049/cje.2021.00.274

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Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy

doi: 10.1049/cje.2021.00.274

1.
School of Information Management, Beijing Information Science and Technology University, Beijing 100192, China
2.
Computer School, Beijing Information Science and Technology University, Beijing 100192, China

Funds: This work was partially supported by the National Social Science Fund (21BTQ079), the Ministry of Education of Humanities and Social Science Project (20YJAZH046), and the National Natural Science Foundation of China (61370139)

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Author Bio:
(corresponding author) was born in 1971. He received the Ph.D. degree in computer application technology from Beijing Institute of Technology, China, in 2005. He is now a Professor with the School of Information and Management, Beijing Information Science and Technology University, Beijing, China. He is a Member of the China Computer Federation, ACM Membership, and Member of the Standing Committee of the Privacy Protection Committee of the China Confidentiality Association. His research interests include information system security, privacy preserving, and natural language processing. (E-mail: kanghaiyan@126.com)

was born in 1997. She received the B.E. degree in information security from Beijing Information Science and Technology University (BISTU) in 2019. She is now an M.S. candidate of cyber security at BISTU. Her research focuses on information security and privacy preserving. (Email: jiyuanrui97@126.com)

was born in 1993. She received the B.E. degree in electrical engineering and the automatization from Shenyang Agricultural University, China. She is now a graduate student in computer technology at BISTU, Beijing, China. Her research focuses on information security and privacy preserving
Received Date: 2021-08-05
Accepted Date: 2022-03-23

Available Online: 2022-06-01

Publish Date: 2022-07-05

Abstract

Abstract

With the popularization and development of social software, more and more people join the social network, which produces a lot of valuable information, but also contains plenty of sensitive privacy information. To achieve the personalized privacy protection of massive social network relational data, a privacy enhancement method for social networks relational data based on personalized differential privacy is proposed. And a dimensionality reduction segmentation sampling (DRS-S) algorithm is proposed to implement this method. First, in order to solve the problem of inefficiency caused by the excessive amount of data in social networks, dimension reduction and segmentation are carried out to divide the data into groups. According to the privacy protection requirements of different users, we adopt sampling method to protect users with different privacy requirements at different levels, so as to realize personalized different privacy. After that, the noise is added to the protected data to satisfy the privacy budget. Then publish the social network data. Finally, the proposed algorithm is compared with the traditional personalized differential privacy (PDP) algorithm and privacy preserving approach based on clustering and noise (PBCN) in real data set, the experimental results demonstrate that the quality of privacy protection and data availability of DRS-S are better than that of PDP algorithm and PBCN algorithm.
- Social network,
- Privacy preserving,
- Dimensionality reduction segmentation,
- Personalized differential privacy,
- DRS-S algorithm

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References(19)

References

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