Abstract
Privacy risks of recommender systems have caused increasing attention. Users’ private data is often collected by probably untrusted recommender system in order to provide high-quality recommendation. Meanwhile, malicious attackers may utilize recommendation results to make inferences about other users’ private data. Existing approaches focus either on keeping users’ private data protected during recommendation computation or on preventing the inference of any single user’s data from the recommendation result. However, none is designed for both hiding users’ private data and preventing privacy inference. To achieve this goal, we propose in this paper a hybrid approach for privacy-preserving recommender systems by combining differential privacy (DP) with randomized perturbation (RP). We theoretically show the noise added by RP has limited effect on recommendation accuracy and the noise added by DP can be well controlled based on the sensitivity analysis of functions on the perturbed data. Extensive experiments on three large-scale real world datasets show that the hybrid approach generally provides more privacy protection with acceptable recommendation accuracy loss, and surprisingly sometimes achieves better privacy without sacrificing accuracy, thus validating its feasibility in practice.
Keywords
- Recommender systems
- Privacy-preserving
- Differential privacy
- Randomized perturbation
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Badsha, S., Yi, X., Khalil, I.: A practical privacy-preserving recommender system. Data Sci. Eng. 1(3), 161–177 (2016)
Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: ICDM, pp. 43–52 (2007)
Dwork, C.: Differential privacy: a survey of results. In: International Conference on Theory and Applications of Models of Computation, pp. 1–19 (2008)
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). doi:10.1007/11681878_14
Elgamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theory 31(4), 469–472 (1985)
Erkin, Z., Veugen, T., Toft, T., Lagendijk, R.L.: Generating private recommendations efficiently using homomorphic encryption and data packing. IEEE Trans. Inf. Forensics Secur. 7(3), 1053–1066 (2012)
Guerraoui, R., Kermarrec, A.M., Patra, R., Taziki, M.: D 2 p: distance-based differential privacy in recommenders. VLDB 8(8), 862–873 (2015)
Huang, Y., Evans, D., Katz, J., Malka, L.: Faster secure two-party computation using garbled circuits. In: USENIX Security Symposium, vol. 201 (2011)
Liu, S., Liu, A., Liu, G., Li, Z., Xu, J., Zhao, P., Zhao, L.: A secure and efficient framework for privacy preserving social recommendation. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds.) APWeb 2015. LNCS, vol. 9313, pp. 781–792. Springer, Cham (2015). doi:10.1007/978-3-319-25255-1_64
Ma, X., Li, H., Ma, J., Jiang, Q., Gao, S., Xi, N., Lu, D.: Applet: a privacy-preserving framework for location-aware recommender system. Sci. China Inf. Sci. 60(9), 092101 (2017)
Machanavajjhala, A., Korolova, A., Sarma, A.D.: Personalized social recommendations: accurate or private. VLDB 4(7), 440–450 (2011)
McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the netflix prize contenders. In: KDD, pp. 627–636 (2009)
Nikolaenko, V., Ioannidis, S., Weinsberg, U., Joye, M., Taft, N., Boneh, D.: Privacy-preserving matrix factorization. In: CCS, pp. 801–812 (2013)
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). doi:10.1007/3-540-48910-X_16
Polat, H., Du, W.: Privacy-preserving collaborative filtering using randomized perturbation techniques. In: ICDM, pp. 625–628 (2003)
Yao, A.C.C.: How to generate and exchange secrets. In: FOCS, pp. 162–167 (1986)
Zhan, J., Hsieh, C.L., Wang, I.C., Hsu, T.S., Liau, C.J., Wang, D.W.: Privacy-preserving collaborative recommender systems. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(4), 472–476 (2010)
Zhang, S., Ford, J., Makedon, F.: Deriving private information from randomly perturbed ratings. In: SDM, pp. 59–69 (2006)
Acknowledgment
This work was done while the first author was a visiting student at King Abdullah University of Science and Technology (KAUST). Research reported in this publication was partially supported by KAUST and Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61632016, 61402313).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liu, X. et al. (2017). When Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-55753-3_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55752-6
Online ISBN: 978-3-319-55753-3
eBook Packages: Computer ScienceComputer Science (R0)