Practical Privacy-Preserving K-means Clustering
Payman Mohassel, Mike Rosulek, Ni Trieu
Abstract
Clustering is a common technique for data analysis, which aims to partition data into similar groups. When the data comes from different sources, it is highly desirable to maintain the privacy of each database. In this work, we study a popular clustering algorithm (K-means) and adapt it to the privacypreserving context.
Topics & Concepts
Cluster analysisComputer scienceData miningConstruct (python library)Context (archaeology)Partition (number theory)Protocol (science)Euclidean distancePoint (geometry)ComputationTheoretical computer scienceAlgorithmMachine learningArtificial intelligenceMathematicsComputer networkMedicineCombinatoricsBiologyAlternative medicineGeometryPaleontologyPathologyPrivacy-Preserving Technologies in DataCryptography and Data SecurityRandom Matrices and Applications