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Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations.

D. Wang, A. Smith, и J. Xu. ALT, том 98 из Proceedings of Machine Learning Research, стр. 897-902. PMLR, (2019)

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David Smith

Stephen Smith

Veronica Smith

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Annette Smith

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Scalable Multiparty Computation with Nearly Optimal Work and Resilience.I. Damgård, Y. Ishai, M. Krøigaard, J. Nielsen, и A. Smith. CRYPTO, том 5157 из Lecture Notes in Computer Science, стр. 241-261. Springer, (2008)From Soft Classifiers to Hard Decisions: How fair can we be?R. Canetti, A. Cohen, N. Dikkala, G. Ramnarayan, S. Scheffler, и A. Smith. CoRR, (2018)Privacy-preserving statistical estimation with optimal convergence rates.A. Smith. STOC, стр. 813-822. ACM, (2011)Privacy-Preserving Public Information for Sequential Games.A. Blum, J. Morgenstern, A. Sharma, и A. Smith. ITCS, стр. 173-180. ACM, (2015)Distributed Differential Privacy via Shuffling.A. Cheu, A. Smith, J. Ullman, D. Zeber, и M. Zhilyaev. EUROCRYPT (1), том 11476 из Lecture Notes in Computer Science, стр. 375-403. Springer, (2019)Manipulation Attacks in Local Differential Privacy.A. Cheu, A. Smith, и J. Ullman. CoRR, (2019)Distributed Differential Privacy via Mixnets.A. Cheu, A. Smith, J. Ullman, D. Zeber, и M. Zhilyaev. CoRR, (2018)Toward Privacy in Public Databases.S. Chawla, C. Dwork, F. McSherry, A. Smith, и H. Wee. TCC, том 3378 из Lecture Notes in Computer Science, стр. 363-385. Springer, (2005)Efficient Two Party and Multi Party Computation Against Covert Adversaries.V. Goyal, P. Mohassel, и A. Smith. EUROCRYPT, том 4965 из Lecture Notes in Computer Science, стр. 289-306. Springer, (2008)Control, Confidentiality, and the Right to be Forgotten.A. Cohen, A. Smith, M. Swanberg, и P. Vasudevan. CoRR, (2022)

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