{"data":{"id":"41370701-3f74-4fd7-9f07-5dca2910c188","title":"Using private data with freedom: A cloud-assisted ID-Private data join protocol for privacy-preserving machine learning over distributed data","summary":"This research paper proposes a cloud-assisted protocol for privacy-preserving machine learning that allows AI models to be trained on distributed data (data stored in different locations) without exposing users' private information. The protocol uses ID-Private data joins, a technique that matches data from different sources while keeping sensitive details hidden from the cloud and other parties involved.","solution":"N/A -- no mitigation discussed in source.","labels":["research","privacy"],"sourceUrl":"https://www.sciencedirect.com/science/article/pii/S2214212626001973?dgcid=rss_sd_all","publishedAt":"2026-07-11T12:01:51.645Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}