Abstract
By combining user feedback on items with social networks, cross-domain social recommendations provide users with more accurate recommendation results. However, traditional cross-domain social recommendations require holding both data of ratings and social networks, which is not easy to achieve for both information-oriented and social-oriented websites. To promote cross-domain social network collaboration among the institutions holding different data, this chapter proposes a federated cross-domain social recommendation (FCSR) algorithm. The main innovation is applying Random Response mechanism to achieve sparsely maintained differential privacy for user connections and proposing Matrix Confusion Method to achieve efficient encrypted user feature vector updates. Our experiments on three datasets show the practicality of FCSR in social recommendation and significantly outperforms baselines.
Supported by the National Natural Science Foundation of China (project numbers 62072109 and U1804263).
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References
Bogdanov, D., Laur, S., Willemson, J.: Sharemind: a framework for fast privacy-preserving computations. In: Jajodia, S., Lopez, J. (eds.) ESORICS 2008. LNCS, vol. 5283, pp. 192–206. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88313-5_13
Chai, D., Wang, L., Chen, K., Yang, Q.: Secure federated matrix factorization. IEEE Intell. Syst. 36, 11–20 (2020). https://doi.org/10.1109/MIS.2020.3014880
Cui, J., Chen, C., Lyu, L., Yang, C., Li, W.: Exploiting data sparsity in secure cross-platform social recommendation. In: Advances in Neural Information Processing Systems, vol. 34, pp. 10524–10534 (2021)
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our data, ourselves: privacy via distributed noise generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486–503. Springer, Heidelberg (2006). https://doi.org/10.1007/11761679_29
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14
Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)
Fan, W., et al.: Graph neural networks for social recommendation. In: The World Wide Web Conference, pp. 417–426 (2019)
Guo, G., Zhang, J., Yorke-Smith, N.: Trustsvd: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)
Liu, Y., Liang, C., He, X., Peng, J., Zheng, Z., Tang, J.: Modelling high-order social relations for item recommendation. IEEE Trans. Knowl. Data Eng. 34, 4385–4397 (2020)
Liu, Z., Yang, L., Fan, Z., Peng, H., Yu, P.: Federated social recommendation with graph neural network. ACM Trans. Intell. Syst. Technol. 13, 1–24 (2022). https://doi.org/10.1145/3501815
McSherry, F., Talwar, K.: Mechanism design via differential privacy, pp. 94–103 (2007). https://doi.org/10.1109/FOCS.2007.66
McSherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 19–30 (2009)
Meng, X.: Scalable simple random sampling and stratified sampling. In: International Conference on Machine Learning, pp. 531–539. PMLR (2013)
Mironov, I.: Rényi differential privacy. In: 2017 IEEE 30th Computer Security Foundations Symposium (CSF), pp. 263–275. IEEE (2017)
Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48910-X_16
Raymond, J.F., Stiglic, A.: Security issues in the diffie-hellman key agreement protocol. IEEE Trans. Inf. Theory 22, 1–17 (2000)
Wang, X., He, X., Nie, L., Chua, T.S.: Item silk road: recommending items from information domains to social users. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 185–194 (2017)
Wu, C., Wu, F., Cao, Y., Huang, Y., Xie, X.: FedGNN: federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925 (2021)
Yang, L., Tan, B., Zheng, V., Chen, K., Yang, Q.: Federated Recommendation Systems, pp. 225–239 (2020). https://doi.org/10.1007/978-3-030-63076-8_16
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10, 1–19 (2019)
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Cai, J., Liu, Y., Liu, X., Li, J., Zhuang, H. (2023). Privacy-Preserving Federated Cross-Domain Social Recommendation. In: Goebel, R., Yu, H., Faltings, B., Fan, L., Xiong, Z. (eds) Trustworthy Federated Learning. FL 2022. Lecture Notes in Computer Science(), vol 13448. Springer, Cham. https://doi.org/10.1007/978-3-031-28996-5_11
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