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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2853-2881 |
| Number of pages | 29 |
| Journal | World Wide Web |
| Volume | 22 |
| Issue number | 6 |
| DOIs | |
| State | Published - Nov 1 2019 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Hardware and Architecture
- Computer Networks and Communications
Cite this
}
Towards privacy preserving social recommendation under personalized privacy settings. / Meng, Xuying; Wang, Suhang; Shu, Kai; Li, Jundong; Chen, Bo; Liu, Huan; Zhang, Yujun.
In: World Wide Web, Vol. 22, No. 6, 01.11.2019, p. 2853-2881.Research output: Contribution to journal › Article
TY - JOUR
T1 - Towards privacy preserving social recommendation under personalized privacy settings
AU - Meng, Xuying
AU - Wang, Suhang
AU - Shu, Kai
AU - Li, Jundong
AU - Chen, Bo
AU - Liu, Huan
AU - Zhang, Yujun
PY - 2019/11/1
Y1 - 2019/11/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85049886244&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049886244&partnerID=8YFLogxK
U2 - 10.1007/s11280-018-0620-z
DO - 10.1007/s11280-018-0620-z
M3 - Article
AN - SCOPUS:85049886244
VL - 22
SP - 2853
EP - 2881
JO - World Wide Web
JF - World Wide Web
SN - 1386-145X
IS - 6
ER -