{"data":{"id":"c455d56d-09e3-4011-a771-a05c798cfbb9","title":"TabHGIF: A Unified Hypergraph Influence Framework for Efficient Unlearning in Tabular Data","summary":"TabHGIF is a hypergraph-aware unlearning framework for tabular data that represents rows and attributes as a row-attribute hypergraph to capture high-order feature correlations. It introduces a Hypergraph Influence Function (HGIF) to approximate parameter shifts from structural edits, enabling one-shot parameter correction without re-accessing the raw forget samples. On three benchmarks and four hypergraph backbones, it reaches post-unlearning accuracy close to full retraining, with 2.18–4.27× speedups for row unlearning and 5.05–7.67× for column unlearning, and MIA-AUC values of 0.5014–0.5464.","solution":"N/A -- no mitigation discussed in source.","labels":["research","privacy"],"sourceUrl":"http://ieeexplore.ieee.org/document/11570209","publishedAt":"2026-06-18T13:16:36.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-06-18T13:16:36.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}