{"data":{"id":"19c896b4-2c58-4e94-9168-658625c3c1e6","title":"Learning to Defend: Auto-Augmentation Search Against Model Inversion Attacks","summary":"Model Inversion Attacks (MIAs, where attackers recover private training data by accessing a model's weights or outputs) pose serious privacy risks, and existing defenses don't protect well against attackers with different levels of knowledge. The paper proposes DAAS (Defense via Auto-Augmentation Search), which automatically finds the best combinations of data augmentation (transformations like cropping applied to images) that balance privacy protection and model usefulness better than current methods.","solution":"The source proposes DAAS (Defense via Auto-Augmentation Search), which automatically assesses and identifies augmentation candidates with strong privacy-utility trade-offs from a large augmentation pool. The final search results can then be leveraged for privacy-preserving training against MIAs.","labels":["research","security"],"sourceUrl":"http://ieeexplore.ieee.org/document/11510512","publishedAt":"2026-05-06T13:20:54.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["data_extraction"],"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":"2026-05-06T13:20:54.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.88,"researchCategory":"peer_reviewed","atlasIds":null}}