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Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations.

, , and . ALT, volume 98 of Proceedings of Machine Learning Research, page 897-902. PMLR, (2019)

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Scalable Multiparty Computation with Nearly Optimal Work and Resilience., , , , and . CRYPTO, volume 5157 of Lecture Notes in Computer Science, page 241-261. Springer, (2008)Multiparty computation unconditionally secure against Q^2 adversary structures, and . CoRR, (1999)Calibrating Noise to Sensitivity in Private Data Analysis., , , and . J. Priv. Confidentiality, 7 (3): 17-51 (2016)Distributed Differential Privacy via Shuffling., , , , and . EUROCRYPT (1), volume 11476 of Lecture Notes in Computer Science, page 375-403. Springer, (2019)Manipulation Attacks in Local Differential Privacy., , and . CoRR, (2019)Distributed Differential Privacy via Mixnets., , , , and . CoRR, (2018)Privacy-preserving statistical estimation with optimal convergence rates.. STOC, page 813-822. ACM, (2011)From Soft Classifiers to Hard Decisions: How fair can we be?, , , , , and . CoRR, (2018)Efficient Two Party and Multi Party Computation Against Covert Adversaries., , and . EUROCRYPT, volume 4965 of Lecture Notes in Computer Science, page 289-306. Springer, (2008)Toward Privacy in Public Databases., , , , and . TCC, volume 3378 of Lecture Notes in Computer Science, page 363-385. Springer, (2005)