{"data":{"id":"48f9642f-2075-4c2e-99a8-efae5734b19b","title":"Personalized differential privacy for high-dimensional data: A random sampling and pruning privacy tree approach","summary":"This paper discusses differential privacy (DP, a mathematical method that adds noise to data to protect individual privacy while keeping data useful), which is stronger than traditional anonymization techniques like generalization and suppression. The authors address a key challenge: existing DP methods struggle with high-dimensional data (datasets with many features) and treat all data features equally, even though real-world data has varying privacy needs, such as medical records where disease diagnoses need more protection than age.","solution":"N/A -- no mitigation discussed in source.","labels":["security","privacy"],"sourceUrl":"https://www.sciencedirect.com/science/article/pii/S016740482600043X?dgcid=rss_sd_all","publishedAt":"2026-03-16T20:12:19.529Z","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":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}