{"data":{"id":"fa0d602c-493c-4a4f-b0de-5152bdd93def","title":"Metrics for Privacy-Preserving Generative Models: A Comprehensive Survey","summary":"This academic survey paper examines metrics, or measurement methods, used to evaluate privacy-preserving generative models (AI systems that create new data while protecting personal information). The paper provides a comprehensive overview of different ways researchers measure how well these models protect privacy while still functioning effectively.","solution":"N/A -- no mitigation discussed in source.","labels":["research","privacy"],"sourceUrl":"https://dl.acm.org/doi/abs/10.1145/3815777?af=R","publishedAt":"2026-06-24T12:01:14.580Z","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":"moderate","impactType":["confidentiality"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}