Round-Efficient Secure Inference Based on Masked Secret Sharing for Quantized Neural Network

Autor:	Weiming Wei, Chunming Tang, Yucheng Chen
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	secure inference quantized neural network masked secret sharing Science Astrophysics QB460-466 Physics QC1-999
Zdroj:	Entropy, Vol 25, Iss 2, p 389 (2023)
Druh dokumentu:	article
ISSN:	1099-4300
DOI:	10.3390/e25020389
Popis:	Existing secure multiparty computation protocol from secret sharing is usually under this assumption of the fast network, which limits the practicality of the scheme on the low bandwidth and high latency network. A proven method is to reduce the communication rounds of the protocol as much as possible or construct a constant-round protocol. In this work, we provide a series of constant-round secure protocols for quantized neural network (QNN) inference. This is given by masked secret sharing (MSS) in the three-party honest-majority setting. Our experiment shows that our protocol is practical and suitable for low-bandwidth and high-latency networks. To the best of our knowledge, this work is the first one where the QNN inference based on masked secret sharing is implemented.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/db861b3830f341d69d0d901a8e3d1baf Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.