{"data":{"id":"d8f036b0-5618-4ca0-aaf9-05bfcd68e78f","title":"A Differentially Private Weighted Empirical Risk Minimization Procedure and Its Application to Outcome Weighted Learning","summary":"The paper proposes the first differentially private (DP) algorithm for general weighted empirical risk minimization (wERM), where individual contributions to the objective vary, and derives its privacy guarantees and empirical and population utility bounds. It applies this framework to outcome-weighted learning (OWL) for individualized treatment rules. Experiments on simulated and real data show that OWL trained via DP-wERM keeps strong DP guarantees with robust performance.","solution":"N/A -- no mitigation discussed in source.","labels":["privacy","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11579412","publishedAt":"2026-06-25T13:17:25.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-06-25T13:17:25.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["confidentiality"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.8,"researchCategory":"peer_reviewed","atlasIds":null}}