Abstract
K-nearest neighbors (KNN) has been successfully used for recommendation, but querying neighbors of high quality is nearly impossible when the feature space is small or has limited training data. However, due to privacy requirements and government policies, directly transferring data from one data owner to another is not workable. Therefore, we propose a novel KNN approach, secured federated KNN (SF-KNN), that takes privacy requirements into consideration and builds a federated model to gain global neighbors with joint parties, in order to improve the model performance. Specifically, it empowers the parties to train high-quality models with little data. More importantly, it makes cross-domain training possible. We implement SF-KNN on Euclidean and cosine metrics using user-based and item-based methods. In our experiment, we evaluate the proposed SF-KNN on three data sources, MovieLens, Netflix, and Amazon, and several diverse domains, movies, books, clothes, jewellery and food, by comparing it against various baselines. The experiment results indicate that SF-KNN is able to learn more precise neighbors than a local KNN trained by parties individually. In general, it outperforms the local KNN on all of the datasets, reaching 15% average accuracy gain on the Euclidean metric and 8% of it on the cosine metric when simulating 10 parties across all data sources.
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References
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 439–450 (2000)
Bennett, J., Lanning, S., et al.: The netflix prize. In: Proceedings of KDD Cup and Workshop, vol. 2007, p. 35. Citeseer (2007)
Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175–1191 (2017)
Chai, D., Wang, L., Chen, K., Yang, Q.: Secure federated matrix factorization. arXiv preprint arXiv:1906.05108 (2019)
Chen, H., Chillotti, I., Dong, Y., Poburinnaya, O., Razenshteyn, I., Riazi, M.S.: \(\{\)SANNS\(\}\): scaling up secure approximate k-nearest neighbors search. In: 29th \(\{\)USENIX\(\}\) Security Symposium (\(\{\)USENIX\(\}\) Security 20) (2020)
Chen, H., Laine, K., Rindal, P.: Fast private set intersection from homomorphic encryption. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1243–1255 (2017)
Du, W., Han, Y.S., Chen, S.: Privacy-preserving multivariate statistical analysis: linear regression and classification. In: Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 222–233. SIAM (2004)
Elmehdwi, Y., Samanthula, B.K., Jiang, W.: Secure k-nearest neighbor query over encrypted data in outsourced environments. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 664–675. IEEE (2014)
Fernández-Tobías, I., Cantador, I., Kaminskas, M., Ricci, F.: Cross-domain recommender systems: a survey of the state of the art. In: Spanish Conference on Information Retrieval, pp. 1–12. (2012)
Hardy, S., et al.: Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677 (2017)
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TIIS) 5(4), 1–19 (2015)
He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web, pp. 507–517 (2016)
Hsu, Y.C., Hsueh, C.H., Wu, J.L.: A privacy preserving cloud-based K-NN search scheme with lightweight user loads. Comput. 9(1), 1 (2020)
Kesarwani, M., et al.: Efficient secure k-nearest neighbours over encrypted data. In: EDBT, pp. 564–575 (2018)
Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)
Li, C.S., Darema, F., Chang, V.: Distributed behavior model orchestration in cognitive internet of things solution. Enterp. Inf. Syst. 12(4), 414–434 (2018)
Lindell, Y.: How to simulate it–a tutorial on the simulation proof technique. In: Lindell, Y. (ed.) Tutorials on the Foundations of Cryptography, pp. 277–346. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57048-8_6
Liu, B., Wang, L., Liu, M.: Lifelong federated reinforcement learning: a learning architecture for navigation in cloud robotic systems. IEEE Robot. Autom. Lett. 4(4), 4555–4562 (2019)
Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: International Conference on the Theory and Applications of Cryptographic Techniques, pp. 223–238. Springer (1999). https://doi.org/10.1007/3-540-48910-X_16
Qi, Y., Atallah, M.J.: Efficient privacy-preserving k-nearest neighbor search. In: 2008 The 28th International Conference on Distributed Computing Systems, pp. 311–319. IEEE (2008)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)
Shannon, C.E.: Communication theory of secrecy systems. Bell Syst. Tech. J. 28(4), 656–715 (1949)
Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.S.: Federated multi-task learning. In: Advances in Neural Information Processing Systems, pp. 4424–4434 (2017)
Voigt, P., Von dem Bussche, A.: The EU General Data Protection Regulation (GDPR). A Practical Guide, 1st edn. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57959-7
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019)
Zhan, J.Z., Chang, L., Matwin, S.: Privacy preserving k-nearest neighbor classification. IJ Netw. Secur. 1(1), 46–51 (2005)
Zhuo, H.H., Feng, W., Xu, Q., Yang, Q., Lin, Y.: Federated reinforcement learning. arXiv preprint arXiv:1901.08277 (2019)
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Liu, Z., Wang, L., Chen, K. (2021). Secure Efficient Federated KNN for Recommendation Systems. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_195
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