Abstract
In recommender systems, collaborative filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. Such techniques are based on filtering or evaluating items through the opinions of online consumers. They use patterns learned from their behavior or preferences to make recommendation. In this context, it is of great importance to protect users’ privacy when there is a need to publish data for a specific purpose which conduct to the usefulness of collaborative recommender systems. However, too much protection to individual privacy will lead to the loss of data utility. How to balance between privacy and utility is challenging. In this paper, we propose a privacy-preserving method based on k-means and k-coRating privacy-preserving model. First, we evaluate the k-coRated model by privacy and utility. Then, according to the drawbacks of it, we introduce our solutions to address the problem. Finally, we make a comparison between our model and k-coRated model in different aspects. As a result, our model outperforms k-coRated model with respect to utility as well as privacy.
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- 1.
Netflix. 2006. Netflix Prize. Retrieved December 17, 2018 from https://www.netflixprize.com/.
- 2.
Netflix Sued for Largest Voluntary Privacy Breach To Date. Retrieved December 17, 2018 from https://privacylaw.proskauer.com/2009/12/articles/invasion-of-privacy/.
References
Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: IEEE Symposium on Security and Privacy, SP 2008, pp. 111–125 (2008)
Lohr, S.: Netflix cancels contest after concerns are raised about privacy, p. B3. New York Times (2010)
El Haddad, G., Aïmeur, E., Hage, H.: Understanding trust, privacy and financial fears in online payment. In: 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), pp. 28–36 (2018)
Gai, K., Qiu, M., Xiong, Z., Liu, M.: Privacy-preserving multi-channel communication in edge-of-things. Future Gener. Comput. Syst. 85, 190–200 (2018)
Barth, S., de Jong, M.: The privacy paradox–investigating discrepancies between expressed privacy concerns and actual online behavior–a systematic literature review. Telemat. Inform. 34, 1038–1058 (2017)
Zhang, F., Lee, V.E., Jin, R.: k-CoRating: filling up data to obtain privacy and utility. In: AAAI, pp. 320–328 (2014)
Ye, Y., Wang, L., Han, J., Qiu, S., Luo, F.: An anonymization method combining anatomy and permutation for protecting privacy in microdata with multiple sensitive attributes. In: 2017 International Conference on Machine Learning and Cybernetics (ICMLC), pp. 404–411 (2017)
Zhang, F., Lee, V.E., Jin, R., Garg, S., Choo, K.-K.R., Maasberg, M., et al.: Privacy-aware smart city: a case study in collaborative filtering recommender systems. J. Parallel Distrib. Comput. 127, 145–159 (2018)
Zhang, Q., Liu, H., Wu, Y., Lin, C., Lin, G.: Grey maximum distance to average vector based on quasi identifier attribute. In 2017 International Conference on Grey Systems and Intelligent Services (GSIS), p. 119 (2017)
Bos, J.W., Castryck, W., Iliashenko, I., Vercauteren, F.: Privacy-friendly forecasting for the smart grid using homomorphic encryption and the group method of data handling. In: International Conference on Cryptology in Africa, pp. 184–201 (2017)
Wu, L., Chen, B., Zeadally, S., He, D.: An efficient and secure searchable public key encryption scheme with privacy protection for cloud storage. Soft Comput. 22, 1–12 (2018)
Raisaro, J.L., Choi, G., Pradervand, S., Colsenet, R., Jacquemont, N., Rosat, N., et al.: Protecting privacy and security of genomic data in I2B2 with homomorphic encryption and differential privacy. IEEE/ACM Trans. Comput. Biol. Bioinf. 15, 1413–1426 (2018)
Cappiello, C., Plebani, P., Vitali, M.: A data utility model for data-intensive applications in fog computing environments. In: Mahmood, Z. (ed.) Fog Computing, pp. 183–202. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94890-4_9
Han, J., Yu, J., Lu, J., Peng, H., Wu, J.: An anonymization method to improve data utility for classification. In: International Symposium on Cyberspace Safety and Security, pp. 57–71 (2017)
Brickell, J., Shmatikov, V.: The cost of privacy: destruction of data-mining utility in anonymized data publishing. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 70–78 (2008)
Johnson, M.P., Zhao, L., Chakraborty, S.: Achieving Pareto-optimal MI-based privacy-utility tradeoffs under full data. IEEE J. Sel. Top. Signal Process. 12, 1093–1105 (2018)
Li, T., Li, N.: On the tradeoff between privacy and utility in data publishing. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 517–526 (2009)
Rastogi, V., Suciu, D., Hong, S.: The boundary between privacy and utility in data publishing. In: Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 531–542 (2007)
Batmaz, Z., Kaleli, C.: Methods of privacy preserving in collaborative filtering. In: 2017 International Conference on Computer Science and Engineering (UBMK), pp. 261–266 (2017)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 10, 557–570 (2002)
Machanavajjhala, A., Gehrke, J., Kifer, D.: ℓ-density: privacy beyond k-anonymity. In: Proceedings of the International Conference on Data Engineering (ICDE 2006), Atlanta, Georgia (2006)
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: IEEE 23rd International Conference on Data Engineering, ICDE 2007, pp. 106–115 (2007)
Dwork, C.: Differential privacy: a survey of results. In: International Conference on Theory and Applications of Models of Computation, pp. 1–19 (2008)
Chen, K.-C., Yu, C.-M., Tai, B.-C., Li, S.-C., Tsou, Y.-T., Huang, Y., et al.: Data-driven approach for evaluating risk of disclosure and utility in differentially private data release. In: 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), pp. 1130–1137 (2017)
Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 580–585 (1985)
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Xiang, Z., El-Haddad, G., Aïmeur, E. (2019). Privacy vs. Utility: An Enhanced K-coRated. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11619. Springer, Cham. https://doi.org/10.1007/978-3-030-24289-3_42
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