Hanieh Totonchi Asl, Ebrahim Nouri, Zhuoran Li, Danella Zhao · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27431 · open-access PDF
Private-preserving machine learning (PPML) bietet einen vielversprechenden Weg, um leistungsstarke AI-Dienste an die Spitze zu bringen, ohne sensible Benutzerdaten zu entlarven. Jedoch bleibt seine praktische Bereitstellung durch die hohen Kommunikations-, Berechnungs- und Datenbewegungskosten sicherer Multi-Party-Computing (MPC) eingeschränkt, insbesondere während der nichtlinearen Schlussfolgerung. Bestehende MPC-Systeme trennen typischerweise die unabhängige, korrelierte Zufallserzeugung von der Online-Ausführung, wodurch erheblicher Speicherdruck entsteht, um einmalige kryptografische Daten zu buchen.
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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