基于个性化隐私保护的协同过滤算法

差分隐私可以有效解决推荐系统的隐私泄露问题，但是其引入的噪声会降低推荐系统的性能.此外，不同用户对隐私保护的敏感性是不同的，考虑用户的个性化需求可以减少加入的噪声，有助于提高推荐系统性能.综合以上两个维度，在考虑用户评分敏感性的基础上，提出了一种个性化的差分隐私保护协同过滤算法.算法在用户本地划分评分的隐私敏感度，并采用随机翻转机制对隐私敏感评分进行隐私保护.服务器获取扰动后的数据，利用贝叶斯估计方法重构项目之间的联合分布以提高算法的推荐准确性.理论和实验结果表明，算法在保护用户隐私安全的同时具有良好的推荐性能.

Collaborative Filtering Algorithm Based on Personalized Privacy Protection

Differential privacy can effectively solve the privacy leakage problem of recommended systems, but introduced noise will reduce the performance of recommended systems. In addition, different users have different sensitivities to privacy protection. So, considering individual needs of users, an algorithm can be designed to reduce the added noise and help improve the performance of the recommended system. In this paper, combining the above two dimensions, a personalized differential privacy-preserving collaborative filtering algorithm was proposed. Firstly, dividing the privacy sensitivity of the ratings locally, the algorithm was designed to use a random flip mechanism to protect the privacy of the privacy-sensitive scores. Then, according to the obtained perturbed data, the algorithm was arranged to use a Bayesian estimation method to reconstruct the joint distribution between items, so as to improve the recommendation accuracy of the algorithm. The theoretical and experimental results show that the algorithm can not only provide better recommendation performance but also protect privacy of users.