S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training

31 de January de 2021

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<meta name="Description" CONTENT="Artificial Intelligence Journal" />
<meta name="r0identifier" content="94359e7131c1e44cc3a11f4a719649be" />
RxRegistration IDNº
R0Hash MD5 (of R3):94359e7131c1e44cc3a11f4a719649be
R1Registration number (in the domain editorialia.com at WordPress):dmeditorialiawp.30220
R2Date-p-order (ddmmyyyypx): 31012021p1
R3Cid (combined id R1+R2):dmeditorialiawp.3022031012021p1
R4Resource official title:S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training
R5Publisher:arXiv.org
R6Resource website (1) ( #OpenAccess | #Openscience ): arxiv.org/abs/2101.12078
R12Authors (separated by commas):Prashanthi Ramachandran, Shivam Agarwal, Arup Mondal, Aastha Shah, Debayan Gupta
R14Keyword (selected 1 among the labels applied to this entry):=NeuralNetworks
R15QR code (of the linked url at WP):
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R17Digital signature URL:Pending signature
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