满足本地化差分隐私的推荐系统中隐私预算的优化设置

Recommendation system can help users find the data they need from the massive amounts of data. At the same time, uploading original user data to the server may reveal user privacy. We utilize local differential privacy techniques to provide privacy protection for users in the recommendation system. In the local differential privacy model, the degree of privacy protection is measured by the privacy budget, and a high privacy budget usually means high analysis accuracy. To help users minimize privacy loss and maximize recommendation accuracy, we model the privacy budget setting problem as a multiarmed bandit problem and propose the upper confidence bound learning policy to help each user choose the privacy budget. Considering that users have different sensitivity levels to different data, we modify the above policy. Experimental results reveal that the proposed policy can help users choose an appropriate privacy budget, which can effectively increase the total user payoff.