{"data":{"id":"ed45229c-f6a2-4b14-8a14-651ce5b8ac58","title":"FedNSA: Boosting Secure Aggregation by Assembling Differentially Private Noise Shares","summary":"Federated learning (FL, where multiple devices train AI models together without sharing raw data) faces privacy risks because adversaries can extract sensitive information from model updates. FedNSA is a new protocol that combines differential privacy (adding mathematical noise to hide individual data patterns), encryption, and multi-party computation (MPC, a technique where multiple parties jointly compute results without revealing their individual inputs) to protect model updates while reducing the communication and computational burden that makes secure aggregation impractical on resource-constrained devices like smartphones.","solution":"N/A -- no mitigation discussed in source.","labels":["security","privacy"],"sourceUrl":"http://ieeexplore.ieee.org/document/11480203","publishedAt":"2026-04-13T13:17:12.000Z","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":"2026-04-13T13:17:12.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.88,"researchCategory":"peer_reviewed","atlasIds":null}}