Abstract
Collaborative filtering has been a popular Recommendation algorithm which received significant attention in recent years; however, privacy issues in these systems cannot be ovelooked. Specifically, K Nearest Neighbor (KNN) collaborative filtering algorithm is prone to KNN attack. The aim of this paper is to integrate cryptography and differential privacy by proposing CryptoDP algorithm. The CryptoDP algorithm helps to tackle the privacy issue in KNN based collaborative filtering. The algorithm includes two privacy preserving operations: Encryption and Perturbation. The ratings given by user are protected by encryption in individual user device. The threat on collaborative filtering algorithm is resolved with the Differential Privacy mechanism. Such mechanism perturbs the similarity score with gaussian noise which enhances the performance of recommendation system with privacy. The CryptoDP algorithm reduces the magnitude of noise introduced from the traditional differential privacy and existing private algorithms. The experimental results with two real datasets confirm that the cryptoDP algorithm provides a robust privacy guarantee with minimal accuracy loss.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Calandrino, J.A., Kilzer, A., Narayanan, A., Felten, E.W., Shmatikov, V.: ‘You might also like:’ privacy risks of collaborative filtering. In: 2011 IEEE Symposium on Security and Privacy, Oakland, CA, USA, pp. 231–246 (2011). https://doi.org/10.1109/SP.2011.40
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 (KDD), pp. 627-635. Association for Computing Machinery, Inc., Paris, France (2009). https://doi.org/10.1145/1557019.1557090
Friedman, A., Berkovsky, S., Kaafar, M.A.: A differential privacy framework for matrix factorization recommender systems. User Model. User-adapt. Interact. 26, 425–458 (2016)
Zhu, X., Sun, Y.: Differential privacy for collaborative filtering recommender algorithm. In: IWSPA 2016—Proceedings of the 2016 ACM on International Workshop on Security and Privacy Analytics, New Orleans, Louisiana, USA, pp. 9–16 (2016). https://doi.org/10.1145/2875475.2875483
Badsha, S., Yi, X., Khalil, I.: A practical privacy-preserving recommender system. Data Sci. Eng. 1, 161–177 (2016)
Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) Automata, Languages and Programming, ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Berlin, Heidelberg (2006)
Zhu, T., Li, G., Zhou, W., Yu, P.S.: Differential Privacy and Applications, 1st edn. Springer, Heidelberg (2017)
Dwork, C., Roth, A.: The Algorithmic Foundations of Differential Privacy. Now Publishers Inc., Hanover, MA, USA (2013)
Balle, B., Wang, Y.X.: Improving the Gaussian mechanism for differential privacy: analytical calibration and optimal denoising. In: Dy, J., Krause, A. (eds.) 35th International Conference on Machine Learning (ICML 2018), pp. 678–692. PMLR (2018)
Nissim, K., Raskhodnikova, S., Smith, A.: Smooth sensitivity and sampling in private data analysis. In: Proceedings of the Annual ACM Symposium on Theory of Computing, pp. 75–84. ACM Press, San Diego, CA, USA (2007). https://doi.org/10.1145/1250790.1250803
Erkin, Z., Beye, M., Veugen, T., Lagendijk, R.L.: Privacy-preserving content-based recommendations through homomorphic encryption. In: 33rd WIC Symposium on Information Theory, Boekelo, The Netherlands, pp. 71–77 (2012)
Basu, A., Vaidya, J., Kikuchi, H., Dimitrakos, T.: Privacy-preserving collaborative filtering for the cloud. In: Proceedings—2011 3rd International Conference on Cloud Computing Technology and Science (CloudCom), Athens, Greece, pp. 223–230 (2011). https://doi.org/10.1109/CloudCom.2011.38
Kikuchi, H., Aoki, Y., Terada, M., Ishii, K., Sekino, K.: Accuracy of privacy-preserving collaborative filtering based on quasi-homomorphic similarity. In: Proceedings—IEEE 9th International Conference on Ubiquitous Intelligence and Computing IEEE 9th International Conference on Autonomic and Trusted Computing (UIC-ATC 2012), Fukuoka, Japan, pp. 555–562 (2012). https://doi.org/10.1109/UIC-ATC.2012.131
Kikuchi, H., Kizawa, H., Tada, M.: Privacy-preserving collaborative filtering schemes. In: Proceedings—International Conference on Availability, Reliability and Security (ARES 2009), Fukuoka, Japan, pp. 911–916 (2009). https://doi.org/10.1109/ARES.2009.148
Elnabarawy, I., Jiang, W., Wunsch, D.C.: Survey of Privacy-Preserving Collaborative Filtering. arXiv 1–8 (2020)
Canny, J.: Collaborative filtering with privacy. In: Proceedings—IEEE Symposium on Security and Privacy 2002-January, Berkeley, CA, USA, pp. 45–57 (2002)
Li, J., et al.: Enforcing differential privacy for shared collaborative filtering. IEEE Access 5, 35–49 (2017)
Erkin, Z., Beye, M., Veugen, T., Lagendijk, R.L.: Privacy enhanced recommender system. In: Thirty-first Symposium on Information Theory in the Benelux, Rotterdam, Netherlands, pp. 35–42 (2010)
Friedman, A., Knijnenburg, B.P., Vanhecke, K., Martens, L., Berkovsky, S.: Privacy aspects of recommender systems. In: Recommender Systems Handbook, 2nd edn., pp. 649–688. Springer, USA (2015)
Dataset. https://grouplens.org/datasets/movielens/. Accessed 29 Mar 2021
CrypTensor—CrypTen 0.1 documentation n.d. https://crypten.readthedocs.io/en/latest/cryptensor.html. Accessed 29 Mar 2021
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sangeetha, S., Sudha Sadasivam, G., Nithesh, V., Mounish, K. (2022). Confluence of Cryptography and Differential Privacy: A Hybrid Approach for Privacy Preserving Collaborative Filtering. In: Dua, M., Jain, A.K., Yadav, A., Kumar, N., Siarry, P. (eds) Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-5747-4_29
Download citation
DOI: https://doi.org/10.1007/978-981-16-5747-4_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5746-7
Online ISBN: 978-981-16-5747-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)