Privacy vs. Utility: An Enhanced K-coRated

摘要

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.