Collaborative filtering (CF) systems are receiving increasing attention. Data collected from users is needed for CF; however, many users do not feel comfortable to disclose data due to privacy risks. They sometimes refuse to provide information or might decide to give false data. By introducing privacy measures, it is more likely to increase users' confidence to contribute their data and to provide more truthful data. In this paper, we investigate achieving referrals using item-based algorithms on binary ratings without greatly exposing users' privacy. We propose to use randomized response techniques (RRT) to perturb users' data. We conduct experiments to evaluate the accuracy of our scheme and to show how different parameters affect our results using real data sets. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Polat, H., & Du, W. (2006). Achieving private recommendations using randomized response techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3918 LNAI, pp. 637–646). https://doi.org/10.1007/11731139_73
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