{"data":{"id":"9877b64d-243f-4a62-b106-63d31f8ddce4","title":"Privacy-Preserving Model Transcription With Differentially Private Synthetic Distillation","summary":"This research addresses the risk that AI models trained on private data could leak sensitive information if attackers extract data from them. The authors propose a method called differentially private synthetic distillation, which converts a trained model into a privacy-protected version without needing access to the original private data, using a generator to create synthetic data and noise to obscure sensitive patterns.","solution":"N/A -- no mitigation discussed in source.","labels":["research","privacy"],"sourceUrl":"http://ieeexplore.ieee.org/document/11367704","publishedAt":"2026-01-29T13:23:19.000Z","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":"2026-01-29T13:23:19.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}