{"data":{"id":"f33814d3-3a42-457e-b262-6c273ae88fd8","title":"AuditML: An Efficient Publicly Auditable Privacy-Preserving Framework for Machine Learning Inference","summary":"AuditML is a publicly auditable privacy-preserving framework for machine learning inference in the semi-honest model. It combines arithmetic secret sharing with commitment-based audit data, implemented on MP-SPDZ, and evaluated on linear regression, fixed-threshold binary logistic classification, and linear-kernel SVM. At a batch size of 50, linear and logistic inference completed online in under one second, while SVM took 10.0939–40.0334 seconds.","solution":"N/A -- no mitigation discussed in source.","labels":["security","privacy","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11674254","publishedAt":"2026-09-01T13:17:12.000Z","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":"2026-09-01T13:17:12.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality","integrity"],"aiComponentTargeted":"inference","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}