{"data":{"id":"999bd8ac-52f0-4adb-b9ee-ec1d7fff3a1e","title":"Garland: Graph Neural Network-Based Federated Recommendation With Malicious Security via Secret-Shared Shuffle","summary":"Garland is a system for recommendation engines that use graph neural networks (GNNs, which are AI models that learn patterns from interconnected user-item relationships) in federated settings, where data stays on users' devices instead of being sent to one central server. The system addresses a key problem: untrusted servers that help expand users' local data can spy on both item information and user relationships, so Garland uses secret-shared shuffle (a cryptographic technique that mixes data while keeping it encrypted) to protect privacy while still catching if a malicious server tries to cheat.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"http://ieeexplore.ieee.org/document/11523543","publishedAt":"2026-05-19T13:17:32.000Z","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":"2026-05-19T13:17:32.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality","integrity"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}