{"data":{"id":"ed6c0b5c-c966-45ce-9591-5e8ed02f1715","title":"M&M: Secure Two-Party Machine Learning Through Modulus Conversion and Mixed-Mode Protocols","summary":"The paper introduces M&M, a framework for secure two-party machine learning that uses an efficient modulus conversion protocol to run each cryptographic subprotocol in its optimal modulus domain. The authors report a 6x to 100x improvement for approximated truncations with 1-bit error tolerance, an average 5x reduction in communication and 4x reduction in runtime for machine learning functions, and a 25% to 99% cost-efficiency gain for private deep neural network inference alongside a 50% gain for private gradient boosting decision tree training.","solution":"N/A -- no mitigation discussed in source.","labels":["research","privacy"],"sourceUrl":"http://ieeexplore.ieee.org/document/11297783","publishedAt":"2025-12-11T13:17:30.000Z","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":"2025-12-11T13:17:30.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":null,"aiComponentTargeted":"inference","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}