id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-143847-vtwn5mmd	Ryffel, Th'eo	ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing	2020-06-08		.txt	text/plain	6038	379	62	This framework implements semi-honest 2-party computation and leverages function secret sharing, a recent cryptographic protocol that only uses lightweight primitives to achieve an efficient online phase with a single message of the size of the inputs, for operations like comparison and multiplication which are building blocks of neural networks. Secure multiparty computation (SMPC) is a promising technique that can efficiently be integrated into machine learning workflows to ensure data and model privacy, while allowing multiple parties or institutions to participate in a joint project. • We show how these blocks can be used in machine learning to implement operations for secure evaluation and training of arbitrary models on private data, including MaxPool and BatchNorm. Our major contribution to the function secret sharing scheme is regarding comparison (which allows to tackle non-polynomial activation functions for neural networks): we build on the idea of the equality test to provide a synthetic and efficient protocol whose structure is very close from the previous one.	./cache/cord-143847-vtwn5mmd.txt	./txt/cord-143847-vtwn5mmd.txt