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Utility analysis on privacy-preservation algorithms for online social networks: an empirical study

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Abstract

Social networks have gained tremendous popularity recently. Millions of people use social network apps to share precious moments with friends and family. Users are often asked to provide personal information such as name, gender, and address when using social networks. However, as the social network data are collected, analyzed, and re-published at a large scale, personal information might be misused by unauthorized third parties and even attackers. Therefore, extensive research has been carried out to protect the data from privacy violations in social networks. The most popular technique is graph perturbation, which modifies the local topological structure of a social network user (a vertex) via various randomization techniques before the social graph data is published. Nevertheless, graph anonymization may affect the usability of the data as random noises are introduced, decreasing user experience. Therefore, a trade-off between privacy protection and data usability must be sought. In this paper, we employ various graph and application utility metrics to investigate this trade-off. More specifically, we conduct an empirical study by implementing five state-of-the-art anonymization algorithms to analyze the graph and application utilities on a Facebook and a Twitter dataset. Our results indicate that most anonymization algorithms can partially or conditionally preserve the graph and application utilities and any single anonymization algorithm may not always perform well on different datasets. Finally, drawing on the reviewed graph anonymization techniques, we provide a brief overview on future research directions and challenges involved therein.

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Funding

This work was partially supported by the US National Science Foundation under grants CNS-1704397, CNS-1704287, and CNS-1704274; the National Science Foundation of China under grants 61832012, 61871466, and 61771289; and the Guangxi Natural Science Foundation Innovation Research Team Project under Grant 2016GXNSFGA380002.

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Authors and Affiliations

  1. Department of Computer Science, The George Washington University, Washington, DC, USA

    Cheng Zhang, Honglu Jiang & Xiuzhen Cheng

  2. Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing, Yulin Normal University, Yuzhou Qu, China

    Feng Zhao

  3. Department of Computer Science, Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  4. Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA

    Zhi Tian

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  1. Cheng Zhang
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  2. Honglu Jiang
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  3. Xiuzhen Cheng
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  4. Feng Zhao
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Correspondence to Feng Zhao.

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Zhang, C., Jiang, H., Cheng, X. et al. Utility analysis on privacy-preservation algorithms for online social networks: an empirical study. Pers Ubiquit Comput 25, 1063–1079 (2021). https://doi.org/10.1007/s00779-019-01287-0

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