Abstract
The well-known cold start problem in traditional collaborative filtering based recommender systems can be effectively addressed by social recommendation, which has been witnessed by a number of researches recently in many application domains. The social graph used in social recommendation is typically owned by a third party such as Facebook and Twitter, and should be hidden from recommender systems for obvious reasons of commercial benefits, as well as due to privacy legislation. In this paper, we present a secure and efficient framework for privacy preserving social recommendation. Our framework is built on mature cryptographic building blocks, including Paillier cryptosystem and Yao’ protocol, which lays a solid foundation for the security of our framework. Using our framework, the owner of sales data and the owner of social graph can cooperatively compute social recommendation, without revealing their private data to each other. We theoretically prove the security and analyze the complexity of our framework. Empirical study shows our framework has a linear complexity with respect to the number of users and items in recommender systems and is practical in real applications.
Original language | English |
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Title of host publication | Web Technologies and Applications |
Subtitle of host publication | 17th Asia-Pacific Web Conference, APWeb 2015, Proceedings |
Editors | Reynold Cheng, Bin Cui, Zhenjie Zhang, Ruichu Cai, Jia Xu |
Publisher | Springer, Springer Nature |
Pages | 781-792 |
Number of pages | 12 |
Volume | 9313 |
ISBN (Electronic) | 9783319252551 |
ISBN (Print) | 9783319252544 |
DOIs | |
Publication status | Published - 2015 |
Externally published | Yes |
Event | Asia-Pacific Web Conference (17th : 2015) - Guangzhou, China Duration: 18 Sept 2015 → 20 Sept 2015 Conference number: 17th |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9313 |
ISSN (Print) | 03029743 |
ISSN (Electronic) | 16113349 |
Conference
Conference | Asia-Pacific Web Conference (17th : 2015) |
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Abbreviated title | APWeb 2015 |
Country/Territory | China |
City | Guangzhou |
Period | 18/09/15 → 20/09/15 |
Keywords
- Privacy preserving
- Recommender system
- Secure two-party computation
- Social recommendation