{"data":{"id":"14a809a2-3e5c-46b3-8034-96f3ac779928","title":"Secure and Efficient Model Training Framework for Multiuser Semantic Communications via Over-the-Air Mixup","summary":"This paper presents SIMix, a training framework for systems where multiple users learn AI models together over wireless networks while protecting their private data. The system uses Over-the-Air Mixup (OAM, a technique that combines data from multiple users through wireless transmission to hide sensitive information) and groups users strategically to reduce communication needs by up to 25% while defending against model inversion attacks (attempts to reconstruct private training data from a trained model) and label inference attacks (guessing what category a user's data belongs to).","solution":"The paper proposes integrating Over-the-Air Mixup with label-aware user grouping, including a closed-form Tx-Rx scaling optimization that minimizes mean square error under channel noise, and an extended max-clique algorithm that dynamically partitions users into groups with minimal intra-label similarity to reduce model inversion attack success rates.","labels":["research","security"],"sourceUrl":"http://ieeexplore.ieee.org/document/11406198","publishedAt":"2026-02-23T13:19:07.000Z","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":"2026-02-23T13:19:07.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality","integrity"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}