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MPC-Friendly Commitments for Publicly Verifiable Covert Sec..:
, In:
Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security
,
Agrawal, Nitin
;
Bell, James
;
Gascón, Adrià
. - p. 2685-2704 , 2021
Link:
https://dl.acm.org/doi/10.1145/3460120.3485375
RT T1 <cTitle>Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security</cTitle>: T1 <Title>MPC-Friendly Commitments for Publicly Verifiable Covert Security</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3485375&Exemplar=1&LAN=DE A1 Agrawal, Nitin A1 Bell, James A1 Gascón, Adrià A1 Kusner, Matt J. PB ACM YR 2021 K1 committed MPC K1 privacy-preserving deep learning K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks K1 Security and privacy K1 Software and application security K1 Domain-specific security and privacy architectures SP 2685 OP 2704 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3460120.3485375 DO https://dl.acm.org/doi/10.1145/3460120.3485375 SF ELIB - SuUB Bremen
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