差分隐私软大间隔聚类

软大间隔聚类(Soft Large Margin Clustering)已被证明比其他诸如K-Means等诸多聚类算法具有更优的聚类性能与可解释性。然而作为单机聚类算法，仍有可扩展性的瓶颈，因此有人将其进行分布式改造。然而在进行分布式运算时，在迭代过程中存在节点之间相互通信的过程。如果某些节点存在隐私数据，那么数据集中...

Differentially Private Soft Large Margin Clustering

Soft large margin clustering (SLMC) has been proven to achieve better accuracy and interpretability than other clustering algorithms. However, as a stand-alone clustering algorithm, it still has scalability bottleneck. So researchers develop it into distributed version. However, when performing distributed learning, there are communications between nodes during iterative process. If some nodes have private data, the sensitive information may be leaked in the communication process. So this paper combines the distributed sparse SLMC (DS-SLMC) with privacy protection and provides zero concentration differential privacy by adding Gaussian noise to develop differentially private SLMC (DP-SLMC). Finally, its privacy protection utility is proved theoretically, and experiments show that it has similar convergence speed and clustering performance compared with non-federated algorithm.