Hanieh Totonchi Asl, Ebrahim Nouri, Zhuoran Li, Danella Zhao · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27431 · open-access PDF
Pembelajaran mesin yang mempertahankan privasi (PPML) menawarkan jalan yang menjanjikan untuk membawa layanan AI yang kuat ke tepi tanpa mengekspos data pengguna yang sensitif. Namun, pengembangan praktisnya tetap dibatasi oleh biaya komunikasi, komputasi, dan pergerakan data yang tinggi dari komputasi multi-pihak (MPC) yang aman, terutama selama inferensi nonlinear. Sistem MPC yang ada biasanya memisahkan input-independent correlated randomness generation dari eksekusi online, menciptakan tekanan memori yang substansial untuk buffer data kriptografis sekali pakai.
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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