A Secure and Efficient Framework for Privacy Preserving Social Recommendation
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.
Keywordsrecommender system social recommendation privacy preserving secure two-party computation
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- 2.Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 4th edn. MIT Press (2009)Google Scholar
- 4.Huang, Y., Evans, D., Katz, J., Malka, L.: Faster secure two-party computation using garbled circuits. USENIX Security (2011)Google Scholar
- 5.Jorgensen, Z., Yu, T.: A privacy-preserving framework for personalized, social recommendations. In: EDBT 2014, pp. 571–582 (2014)Google Scholar
- 7.Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: ACM, pp. 195–202 (2009)Google Scholar
- 8.Liu, B., Hengartner, U.: Privacy-preserving social recommendations in geosocial networks. In: IEEE, pp. 69–76. (2013)Google Scholar
- 11.McSherry, F., Mironov, I.: Differentially private recommender systems: Building privacy into the Netflix prize contenders. In: KDD 2009, pp. 627–636 (2009)Google Scholar
- 12.Nikolaenko, V., Weinsberg, U., Ioannidis, S., Joye, M., Boneh, D., Taft, N.: Privacy-preserving ridge regression on hundreds of millions of records. In: S&P 2013, pp. 334–348 (2013)Google Scholar
- 13.Nikolaenko, V., Weinsberg, U., Ioannidis, S., Joye, M., Boneh, D., Taft, N.: Privacy-Preserving matrix factorization. In: CCS 2013, pp. 801–812 (2013)Google Scholar
- 15.Shokri, R., Pedarsani, P., Theodorakopoulos, G., Hubaux, J.: Preserving privacy in collaborative filtering through distributed aggregation of offline profiles. In: RecSys 2009, pp. 157–164 (2009)Google Scholar
- 16.Yao, A.C.-C.: How to generate and exchange secrets. In: FOCS 1986, pp. 162–167 (1986)Google Scholar
- 17.Yuan, Q., Zhao, S., Chen, L., et al.: Augmenting collaborative recommender by fusing explicit social relationships. In: Recsys (2009)Google Scholar