基于差分隐私保护的模糊C均值聚类推荐

通过对用户进行模糊C均值聚类，使其以不同的隶属度隶属于不同聚类，解决了因硬聚类导致的推荐准确度低的问题，获得更加准确的聚类效果；针对推荐算法的隐私泄露问题，通过将Laplace噪声引入到模糊C均值聚类过程中，实现基于差分隐私保护的模糊C均值聚类推荐.实验结果表明，该算法在保证推荐质量的同时有效改善了推荐系统的安全性.

Fuzzy C-Means Clustering Recommendation Based on Differential Privacy Protection

The users are classified by different membership degrees with fuzzy C-means clustering. A more accurate clustering effect has been obtained and the problem of low recommendation accuracy caused by hard clustering is solved. Aiming at the privacy leakage problem of recommendation algorithm, the Laplace noise is introduced into the fuzzy C-means clustering process, and the differential privacy protection based fuzzy C-means clustering recommendation is implemented. The experimental results show that the proposed algorithm can effectively improve the security of the recommended system with the good quality of the recommendation.