{"data":{"id":"54ddb512-40d0-4c80-b1b3-aed7de03e9f7","title":"Differential Privacy in Practice: Lessons Learned From 10 Years of Real-World Applications","summary":"Differential privacy (DP, a mathematical technique that adds controlled randomness to data to protect individual privacy while keeping data useful) is a widely-used method for protecting sensitive information, but putting it into practice in real-world systems has proven difficult. Researchers analyzed 21 actual deployments of differential privacy by major companies and institutions over the last ten years to understand what works and what doesn't.","solution":"N/A -- no mitigation discussed in source.","labels":["security","privacy"],"sourceUrl":"http://ieeexplore.ieee.org/document/11108240","publishedAt":"2025-08-04T13:16:58.000Z","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":"2025-08-04T13:16:58.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["confidentiality"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}