满足本地化差分隐私的推荐系统中隐私预算的优化设置

Optimized setting of privacy budget in a recommendation system with local differential privacy

推荐系统可帮助用户从众多的数据中发现用户所需数据,与此同时,上传用户原始数据给服务器也可能泄露用户隐私.本文使用本地化差分隐私技术为推荐系统中的用户数据提供隐私保护.在本地化差分隐私模型中,隐私预算控制用户数据的隐私保护程度,较高的隐私预算通常意味着较高的分析准确性.为在最小化隐私损失的同时最大化推荐准确性,我们将隐私预算设置问题建模为多臂赌博机问题,并提出基于置信度上界的学习策略帮助用户选择最优的隐私预算.考虑到用户对不同数据的敏感程度不同,我们对学习策略进行了改进.真实数据集上的实验结果表明,所提策略可以帮助用户选出合适的隐私预算,可有效提高用户的累计收益.

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.