{"data":{"id":"fe9e81f8-7e52-4058-b214-3ac83e34d253","title":"Systematic Literature Review on Differential Privacy in Machine Learning","summary":"This is a systematic literature review, a type of research paper that surveys and analyzes existing studies on differential privacy (a mathematical technique that adds carefully measured noise to data to protect individual privacy) in machine learning. The review examines how researchers are applying differential privacy to train AI models while keeping personal information safe from being extracted or misused.","solution":"N/A -- no mitigation discussed in source.","labels":["research","privacy"],"sourceUrl":"https://dl.acm.org/doi/abs/10.1145/3800684?af=R","publishedAt":"2026-04-18T12:00:28.162Z","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":"training_data","llmSpecific":false,"classifierConfidence":0.92,"researchCategory":"peer_reviewed","atlasIds":null}}