Abstract
The collaborative filtering (CF)-based recommender systems provide recommendations by collecting users’ historical ratings and predicting their preferences on new items. However, this inevitably brings privacy concerns since the collected data might reveal sensitive information of users, when training a recommendation model and applying the trained model (i.e., testing the model). Existing differential privacy (DP)-based approaches generally have non-negligible trade-offs in recommendation utility, and often serve as centralized server-side approaches that overlook the privacy during testing when applying the trained models in practice. In this paper, we propose PrivRec, a user-centric differential private collaborative filtering approach, that provides privacy guarantees both intuitively and theoretically while preserving recommendation utility. PrivRec is based on the locality sensitive hashing (LSH) and the teacher-student knowledge distillation (KD) techniques. A teacher model is trained on the original user data without privacy constraints, and a student model learns from the hidden layers of the teacher model. The published student model is trained without access to the original user data and takes the locally processed data as input for privacy. The experimental results on real-world datasets show that our approach provides promising utility with privacy guarantees compared to the commonly used approaches.
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References
Bai, T., Wen, J.R., Zhang, J., Zhao, W.X.: A neural collaborative filtering model with interaction-based neighborhood. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1979–1982 (2017)
Broder, A.Z., Charikar, M., Frieze, A.M., Mitzenmacher, M.: Min-wise independent permutations. J. Comput. Syst. Sci. 60(3), 630–659 (2000)
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
Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. VLDB 99, 518–529 (1999)
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. International World Wide Web Conferences Steering Committee (2017)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)
Liu, Y., Wang, S., Khan, M.S., He, J.: A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Min. Anal. 1(3), 211–221 (2018)
McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the NetFlix prize contenders. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 627–636. ACM (2009)
Nguyên, T.T., Xiao, X., Yang, Y., Hui, S.C., Shin, H., Shin, J.: Collecting and analyzing data from smart device users with local differential privacy. arXiv preprint arXiv:1606.05053 (2016)
Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I., Talwar, K.: Semi-supervised knowledge transfer for deep learning from private training data. arXiv preprint arXiv:1610.05755 (2016)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_9
Shaked, S., Rokach, L.: Publishing differentially private medical events data. In: Buccafurri, F., Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-ARES 2016. LNCS, vol. 9817, pp. 219–235. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45507-5_15
Vatsalan, D., Sehili, Z., Christen, P., Rahm, E.: Privacy-preserving record linkage for big data: current approaches and research challenges. In: Zomaya, A.Y., Sakr, S. (eds.) Handbook of Big Data Technologies, pp. 851–895. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49340-4_25
Yin, C., Ju, X., Yin, Z., Wang, J.: Location recommendation privacy protection method based on location sensitivity division. EURASIP J. Wirel. Commun. Netw. 2019(1), 266 (2019)
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This work is supported by the National Key Research and Development Program of China.
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Zhang, Y., Gao, N., Chen, J., Tu, C., Wang, J. (2020). PrivRec: User-Centric Differentially Private Collaborative Filtering Using LSH and KD. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_13
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