{"data":{"id":"01d66e20-cabd-4c8f-a740-e2069670c7a8","title":"Unlearning or Not: A Strategic Data Forgetting Scheme for Federated Unlearning With Bounded Rationality","summary":"Federated unlearning (FUL, a process that removes a user's data influence from machine learning models trained across multiple computers) helps protect privacy by letting users exercise their right to be forgotten. This paper proposes a new framework where an FL server uses game theory (a mathematical approach to modeling strategic decision-making) and prospect theory (a model of how people make decisions under uncertainty) to incentivize clients to keep more data during unlearning while preventing selfish behavior.","solution":"N/A -- no mitigation discussed in source.","labels":["research","privacy"],"sourceUrl":"http://ieeexplore.ieee.org/document/11614177","publishedAt":"2026-07-17T13:20:09.000Z","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":"2026-07-17T13:20:09.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.92,"researchCategory":"peer_reviewed","atlasIds":null}}