Studies in health technology and informatics · 2018 · 35 citations · 0 references
ComputingData ScienceHealth InformaticsBig Data AnalyticsData MiningHealth Data AnalyticsData AnonymizationData IntegrationData PartitioningData ProtectionPrivacy ProtectionPrivacy-preserving AnalyticsBig Data ArchitecturePrivacy-preserving Data AnalysisPrivacy-preserving Data AnalyticsPrivacy-preserving Data MiningPrivacy-preserving Health Analytics
Conventional data mining algorithms are unable to satisfy the current requirements on analyzing big data in some fields such as medicine, policy making, judicial, and tax records. However, applying diverse datasets from different institutes (both healthcare and non-healthcare related) can enrich information and insights. So far, analyzing this data in an automated, privacy-preserving manner does not exist to our knowledge. In this work, we propose an infrastructure, and proof-of-concept for privacy-preserving analytics on vertically partitioned data.