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Towards privacy preserving social recommendation under personalized privacy settings

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Abstract

Privacy leakage is an important issue for social relationships-based recommender systems (i.e., social recommendation). Existing privacy preserving social recommendation approaches usually allow the recommender to fully control users’ information. This may be problematic since the recommender itself may be untrusted, leading to serious privacy leakage. Besides, building social relationships requires sharing interests as well as other private information, which may lead to more privacy leakage. Although sometimes users are allowed to hide their sensitive private data using personalized privacy settings, the data being shared can still be abused by the adversaries to infer sensitive private information. Supporting social recommendation with least privacy leakage to untrusted recommender and other users (i.e., friends) is an important yet challenging problem. In this paper, we aim to achieve privacy-preserving social recommendation under personalized privacy settings. We propose PrivSR, a novel privacy-preserving social recommendation framework, in which user can model user feedbacks and social relationships privately. Meanwhile, by allocating different noise magnitudes to personalized sensitive and non-sensitive feedbacks, we can protect users’ privacy against untrusted recommender and friends. Theoretical analysis and experimental evaluation on real-world datasets demonstrate that our framework can protect users’ privacy while being able to retain effectiveness of the underlying recommender system.

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Notes

  1. Facebook provides public pages for products, e.g., https://www.facebook.com/pages/Google-Earth/107745592582048

  2. The idea presented in the extended PrivSR can also be applied to other ranking-based social recommender systems [42] after slight modifications.

  3. http://www.ciao.co.uk/

  4. http://www.epinions.com/

  5. https://techcrunch.com/2009/10/05/twitter-data-analysis-an-investors-perspective-2

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Acknowledgments

This work is supported by, or in part by, National Science Foundation of China (61672500, 61572474), and Program of International S&T Cooperation (2016YFE0121500). Suhang Wang and Huan Liu are supported by the National Science Foundation (NSF) under the grant #1614576 and Office of Naval Research (ONR) under the grant N00014-16-1-2257.

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Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xuying Meng & Yujun Zhang

  2. Department of Computer Science, Arizona State University, Tempe, AZ, USA

    Suhang Wang, Kai Shu, Jundong Li & Huan Liu

  3. Department of Computer Science, Michigan Technological University, Houghton, MI, USA

    Bo Chen

  4. University of Chinese Academy of Sciences, Beijing, China

    Yujun Zhang

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  1. Xuying Meng
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Correspondence to Yujun Zhang.

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A preliminary version of this article was published in AAAI ’18.

This article belongs to the Topical Collection: Special Issue on Social Computing and Big Data Applications

Guest Editors: Xiaoming Fu, Hong Huang, Gareth Tyson, Lu Zheng, and Gang Wang

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Meng, X., Wang, S., Shu, K. et al. Towards privacy preserving social recommendation under personalized privacy settings. World Wide Web 22, 2853–2881 (2019). https://doi.org/10.1007/s11280-018-0620-z

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