Personalized privacy-preserving social recommendation

Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Privacy leakage is an important issue for 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 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 address the problem of achieving privacy-preserving social recommendation under personalized privacy settings. We propose PrivSR, a novel framework for privacy-preserving social recommendation, in which users can model ratings and social relationships privately. Meanwhile, by allocating different noise magnitudes to personalized sensitive and non-sensitive ratings, we can protect users' privacy against the 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.

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Pages3796-3803
Number of pages8
ISBN (Electronic)9781577358008
StatePublished - Jan 1 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/2/182/7/18

Fingerprint

Data privacy
Recommender systems

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Meng, X., Wang, S., Shu, K., Li, J., Chen, B., Liu, H., & Zhang, Y. (2018). Personalized privacy-preserving social recommendation. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 3796-3803). (32nd AAAI Conference on Artificial Intelligence, AAAI 2018). AAAI press.
Meng, Xuying ; Wang, Suhang ; Shu, Kai ; Li, Jundong ; Chen, Bo ; Liu, Huan ; Zhang, Yujun. / Personalized privacy-preserving social recommendation. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. pp. 3796-3803 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).
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abstract = "Privacy leakage is an important issue for 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 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 address the problem of achieving privacy-preserving social recommendation under personalized privacy settings. We propose PrivSR, a novel framework for privacy-preserving social recommendation, in which users can model ratings and social relationships privately. Meanwhile, by allocating different noise magnitudes to personalized sensitive and non-sensitive ratings, we can protect users' privacy against the 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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Meng, X, Wang, S, Shu, K, Li, J, Chen, B, Liu, H & Zhang, Y 2018, Personalized privacy-preserving social recommendation. in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, AAAI press, pp. 3796-3803, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, New Orleans, United States, 2/2/18.

Personalized privacy-preserving social recommendation. / Meng, Xuying; Wang, Suhang; Shu, Kai; Li, Jundong; Chen, Bo; Liu, Huan; Zhang, Yujun.

32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. p. 3796-3803 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Meng X, Wang S, Shu K, Li J, Chen B, Liu H et al. Personalized privacy-preserving social recommendation. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press. 2018. p. 3796-3803. (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).