DPM: Clustering Sensitive Data through Separation. (arXiv:2307.02969v1 [cs.CR])

Privacy-preserving clustering groups data points in an unsupervised manner
whilst ensuring that sensitive information remains protected. Previous
privacy-preserving clustering focused on identifying concentration of point
clouds. In this paper, we take another path and focus on identifying
appropriate separators that split a data set. We introduce the novel
differentially private clustering algorithm DPM that searches for accurate data
point separators in a differentially private manner. DPM addresses two key
challenges for finding accurate separators: identifying separators that are
large gaps …

algorithm clouds clustering data data points focus information novel path point privacy private sensitive data sensitive information