Hanieh Totonchi Asl, Ebrahim Nouri, Zhuoran Li, Danella Zhao · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27431 · open-access PDF
プライバシーを守る機械学習(PPML)は、敏感なユーザーデータを暴露することなく強力なAIサービスを最先端に導入するための有望な道を提供します。しかし、その実用的な展開は、安全なマルチパーティコンピューティング(MPC)の高い通信、計算、およびデータの移動コストによって制限され続けています、特に非線形推定の際に。既存のMPCシステムは、通常、オンライン実行から入力独立のランダム性生成を切り離し、単一使用の暗号データをバッファーするための大量のメモリ圧力を生みます。
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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