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Privacy-Preserving Graph Operations for Social Network Analysis

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Security and Privacy in Social Networks and Big Data (SocialSec 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1298))

Abstract

Nowadays, our daily life is surrounded by various social networks, and they play an important role for people to communicate with others. The social networks contain large amount of valuable information, that can be used for research and business purposes. As a result, social network analysis and data mining receive lots of research attentions in recent years. Graph structure is commonly used in social network analysis, since it is easy to convert the data in social networks into graph-structured data, and various graph algorithms can help to solve different computing problems. In this paper, we investigate performing graph operations in a privacy-preserving manner, which are widely used in social network analysis. We propose two protocols that allow two parties to jointly compute the intersection and union of their graphs. Our protocols utilize homomorphic encryption to prevent information leakage during the process, and we provide security proofs of the protocols in the semi-honest setting.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (61872069) and the Fundamental Research Funds for the Central Universities (N2017012).

An earlier version of this paper was presented at the 22nd Australasian Conference on Information Security and Privacy, 2017 [23].

Author information

Authors and Affiliations

  1. Software College, Northeastern University, Shenyang, China

    Peng Li, Fucai Zhou, Zifeng Xu, Yuxi Li & Jian Xu

  2. School of Information Engineering, Eastern Liaoning University, Dandong, China

    Peng Li

Authors
  1. Peng Li
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  2. Fucai Zhou
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  3. Zifeng Xu
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  4. Yuxi Li
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  5. Jian Xu
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Corresponding author

Correspondence to Fucai Zhou .

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Editors and Affiliations

  1. Swinburne University of Technology, Melbourne, VIC, Australia

    Yang Xiang

  2. Nankai University, Tianjin, China

    Zheli Liu

  3. Guangzhou University, Guangzhou, China

    Jin Li

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Li, P., Zhou, F., Xu, Z., Li, Y., Xu, J. (2020). Privacy-Preserving Graph Operations for Social Network Analysis. In: Xiang, Y., Liu, Z., Li, J. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2020. Communications in Computer and Information Science, vol 1298. Springer, Singapore. https://doi.org/10.1007/978-981-15-9031-3_27

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  • DOI: https://doi.org/10.1007/978-981-15-9031-3_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-9030-6

  • Online ISBN: 978-981-15-9031-3

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