{"data":{"id":"f7856b95-cbe8-40e3-8764-9d198044eb02","title":"<em>Infer-Shield</em>: Defending against membership inference attacks in heterogeneous federated learning via adaptive distillation","summary":"This research paper describes a defense technique called Infer-Shield that protects AI models trained across multiple organizations (federated learning, where different parties train a shared model without sharing raw data) from membership inference attacks (attempts to determine if specific individuals' data was used in training). The paper proposes using adaptive distillation (a technique where a smaller model learns from a larger one to reduce information leakage) as a way to make these distributed AI systems more secure.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"https://www.sciencedirect.com/science/article/pii/S2214212626001419?dgcid=rss_sd_all","publishedAt":"2026-05-25T12:01:00.973Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["membership_inference"],"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":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}