Abstract
Personal social networks are recently used to offer recommendations. Due to privacy concerns, privacy protection while generating accurate referrals is imperative. Since accuracy and privacy are conflicting goals, providing accurate predictions with privacy is challenging. We investigate generating personal social networks-based referrals without greatly jeopardizing users’ privacy using randomization techniques. We perform real data-based trials to evaluate the overall performance of our proposed schemes. We analyze our schemes in terms of privacy and efficiency. Our schemes make it possible to generate accurate recommendations on social networks efficiently while preserving users’ privacy.
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
Preview
Unable to display preview. Download preview PDF.
References
Ben-Shimon, D., Tsikinovsky, A., Rokach, L., et al.: Recommender system from personal social networks. Adv. Soft Comput. 43, 47–55 (2007)
Berkovsky, S., Busetta, P., Eytani, Y., et al.: Collaborative filtering over distributed environment. In: Proc. of the DASUM Workshop, Edinburg, UK (2005)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of the UAI 1998, Madison, WI, USA (1998)
Cranor, L.F.: I didn’t buy it for myself’ privacy and E-commerce personalization. In: Proc. of the ACM Workshop on Privacy in the Electronic Society, Washington, DC, USA (2003)
Canny, J.: Collaborative filtering with privacy. In: Proc. of the IEEE Symposium on Security and Privacy, Oakland, CA, USA (2002)
Canny, J.: Collaborative filtering with privacy via factor analysis. In: Proc. of the ACM SIGIR 2002, Tampere, Finland (2002)
Kaleli, C., Polat, H.: Providing naive Bayesian classifier-based private recommendations on partitioned data. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 515–522. Springer, Heidelberg (2007)
Paillier, P.: Public-key cryptosystems based on composite degree residue classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592. Springer, Heidelberg (1999)
Parameswaran. R.: A robust data obfuscation approach for privacy-preserving collaborative filtering. Georgia Institute of Technology, Atlanta, GA, USA (2006)
Polat, H., Du, W.: Privacy-preserving collaborative filtering. Int. J. Electron. Commer. 9(4), 9–36 (2005)
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)
Polat, H., Du, W.: Effects of inconsistently masked data using RPT on CF with privacy. In: Proc. of the ACM SAC ECT, Seoul, Korea (2007)
Polat, H., Du, W.: Privacy-preserving top-N recommendation on distributed data. J. of the Am. Soc. for Inf. Sci. and Technol. 59(7), 1093–1108 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kaleli, C., Polat, H. (2010). Providing Private Recommendations on Personal Social Networks. In: Snášel, V., Szczepaniak, P.S., Abraham, A., Kacprzyk, J. (eds) Advances in Intelligent Web Mastering - 2. Advances in Intelligent and Soft Computing, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10687-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-10687-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10686-6
Online ISBN: 978-3-642-10687-3
eBook Packages: EngineeringEngineering (R0)