{"data":{"id":"916327a3-c35e-46b6-a406-9f17cafa70bb","title":"Graph Unlearning for MLaaS: Toward Flexible Privacy Adjustment via Influenced Subgraph","summary":"Graph unlearning removes specific information from graph neural networks (GNNs, which are AI models that process data organized as networks of connected nodes). In machine learning-as-a-service (MLaaS, where companies host AI models for users to access), service providers usually cannot see the original training data, making existing unlearning methods impractical. This paper introduces SCGU (subgraph-based certified graph unlearning), a method that lets service providers directly modify model parameters to remove specific information without needing access to the training data, using only a smaller portion of the model related to what needs to be removed.","solution":"N/A -- no mitigation discussed in source.","labels":["research","privacy"],"sourceUrl":"http://ieeexplore.ieee.org/document/11614906","publishedAt":"2026-07-20T13:17:32.000Z","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":"2026-07-20T13:17:32.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":null,"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.92,"researchCategory":"peer_reviewed","atlasIds":null}}