Abstract
In order to contribute to solve the personalization/privacy paradox, we propose a privacy-preserving architecture for one of state-of-the-art recommendation algorithm, Slope One. More precisely, we describe SlopPy (for Slope One with Privacy), a privacy-preserving version of Slope One in which a user never releases directly his personal information (i.e, his ratings). Rather, each user first perturbs locally his information by applying a Randomized Response Technique before sending this perturbed data to a semi-trusted entity responsible for storing it. While there is a trade-off to set between the desired privacy level and the utility of the resulting recommendation, our preliminary experiments clearly demonstrate that SlopPy is able to provide a high level of privacy at the cost of a small decrease of utility.
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References
Basu, A., Vaidya, J., Kikuchi, H.: Perturbation Based Privacy Preserving Slope One Predictors for Collaborative Filtering. In: Dimitrakos, T., Moona, R., Patel, D., McKnight, D.H. (eds.) IFIPTM 2012. IFIP AICT, vol. 374, pp. 17–35. Springer, Heidelberg (2012)
Basu, A., Vaidya, J., Kikuchi, H.: Privacy preserving weighted Slope One predictor for item-based collaborative filtering. In: Proceedings of the International Workshop on Trust and Privacy in Distributed Information Processing (co-organized with IFIPTM 2011) (2011)
Basu, A., Vaidya, J., Kikuchi, H.: Efficient privacy-preserving collaborative filtering based on the weighted Slope One predictor. Journal of Internet Services and Information Security 1(4) (2011)
Das, A., Datar, M., Garg, A.: Google news personalization: Scalable online collaborative filtering. In: Proceedings of the 16th International Conference on World Wide Web (WWW 2007), pp. 271–280 (2007)
Dingledine, R., Mathewson, N., Syverson, P.: Tor: The second-generation onion router. In: Proceedings of the 13th USENIX Security Symposium, pp. 303–320 (2004)
Fredrikson, M., Livshits, B.: RePriv: Re-imagining content personalization and in-browser privacy. In: Proceedings of the 32nd IEEE Symposium on Security and Privacy, pp. 131–146 (2011)
Guha, S., Cheng, B., Francis, P.: Privad: practical privacy in online advertising. In: Proceedings of the 8th USENIX Symposium on Networks, System Design and Implementation (2011)
Kobsa, A.: Privacy-enhanced personalization. Communications of the ACM 50(8), 24–33 (2007)
Lemire, D., Maclachlan, A.: Slope One predictors for online rating-based collaborative filtering. In: Proceedings of the 2005 SIAM International Data Mining Conference (SDM 2005) (2005)
Linden, G., Smith, B., York, J.: Amazon.com recommendations item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: Proceedings of the 29th IEEE Symposium on Security and Privacy, pp. 111–125 (2008)
Olesen, H., Noll, J., Hoffmann, M.: User profiles, personalization and privacy (2009)
Ostrovsky, R., Skeith III, W.E.: A Survey of Single-Database Private Information Retrieval: Techniques and Applications. In: Okamoto, T., Wang, X. (eds.) PKC 2007. LNCS, vol. 4450, pp. 393–411. Springer, Heidelberg (2007)
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)
Pashalidis, A., Preneel, B.: Evaluating tag-based preference obfuscation systems. IEEE Transactions on Knowledge and Data Engineering 24(9), 1613–1623 (2012)
Polat, H., Du, W.: Achieving Private Recommendations Using Randomized Response Techniques. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 637–646. Springer, Heidelberg (2006)
Toubiana, V., Narayanan, A., Boneh, D., Nissenbaum, H., Barocas, S.: Adnostic: Privacy preserving targeted advertising. In: Proceedings of the Network and Distributed System Security Symposium, NDSS 2010 (2010)
Warner, S.L.: Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 60, 63–69 (1965)
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Gambs, S., Lolive, J. (2013). SlopPy: Slope One with Privacy. In: Di Pietro, R., Herranz, J., Damiani, E., State, R. (eds) Data Privacy Management and Autonomous Spontaneous Security. DPM SETOP 2012 2012. Lecture Notes in Computer Science, vol 7731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35890-6_8
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DOI: https://doi.org/10.1007/978-3-642-35890-6_8
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