Scalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detection. (arXiv:2208.06093v1 [cs.LG])

K-means is one of the most widely used clustering models in practice. Due to
the problem of data isolation and the requirement for high model performance,
how to jointly build practical and secure K-means for multiple parties has
become an important topic for many applications in the industry. Existing work
on this is mainly of two types. The first type has efficiency advantages, but
information leakage raises potential privacy risks. The second type is provable
secure but is inefficient and …

application detection fraud fraud detection lg privacy