Hanieh Totonchi Asl, Ebrahim Nouri, Zhuoran Li, Danella Zhao · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27431 · open-access PDF
يوفر التعليم الآلي الذي يحافظ على الخصوصية (PPML) مسارًا ملموسًا نحو إرسال خدمات الذكاء الاصطناعي القوية إلى الأعلى دون إزالة البيانات المستخدمة ذات الصلة. ومع ذلك، لا يزال تطويرها العملية محدودًا من قبل تكلفة الاتصال والتمثيل والتحرك البياني العالية من الحوسبة متعددة الأطراف الآمنة (MPC) ، خاصة خلال التفكير غير الخطير.
Machine translation (Meta M2M100); the English original follows.
Privacy-preserving machine learning (PPML) offers a promising path toward bringing powerful AI services to the edge without exposing sensitive user data. However, its practical deployment remains constrained by the high communication, computation, and data-movement costs of secure multi-party computation (MPC), particularly during nonlinear inference. Existing MPC systems typically decouple input-independent correlated randomness generation from online execution, creating substantial memory pressure to buffer single-use cryptographic data. Moreover, limited storage can force a stop-and-refill
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