基于差分隐私保护的谱聚类算法

针对传统的聚类算法存在隐私泄露的风险，提出一种基于差分隐私保护的谱聚类算法。该算法基于差分隐私模型，利用累计分布函数生成满足拉普拉斯分布的随机噪声，将该噪声添加到经过谱聚类算法计算的样本相似度的函数中，干扰样本个体之间的权重值，实现样本个体间的信息隐藏以达到隐私保护的目的。通过UCI数据集上的仿真实验，表明该算法能够在一定的信息损失度范围内实现有效的数据聚类，也可以对聚类数据进行保护。

Spectral clustering algorithm based on differential privacy protection

Aiming at the problem of privacy leakage in the application of traditional clustering algorithm, a spectral clustering algorithm based on differential privacy protection was proposed. Based on the differential privacy model, the cumulative distribution function was used to generate random noise that satisfies Laplasse distribution. Then the noise was added to the sample similarity function calculated by the spectral clustering algorithm, which disturbed the weight values between the individual samples and realized information hiding between sample individuals for privacy protection. Experimental results of UCI dataset verify that the proposed algorithm can achieve effective data clustering within a certain degree of information loss, and can also protect clustered data.