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On the Protection of Private Information in Machine Learning Systems: Two Recent Approches.

, , , , , , , и . CSF, стр. 1-6. IEEE Computer Society, (2017)

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Angel Mironov

Sergej Mironov

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Maxim Ilyasov

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Другие публикации лиц с тем же именем

Scalable Private Learning with PATE., , , , , и . ICLR, OpenReview.net, (2018)Collision-Resistant No More: Hash-and-Sign Paradigm Revisited.. Public Key Cryptography, том 3958 из Lecture Notes in Computer Science, стр. 140-156. Springer, (2006)Data collection with self-enforcing privacy., , и . CCS, стр. 69-78. ACM, (2006)Application of analysis of algorithms in cryptography.. Stanford University, USA, (2004)On the Protection of Private Information in Machine Learning Systems: Two Recent Approches., , , , , , , и . CSF, стр. 1-6. IEEE Computer Society, (2017)Differentially private recommender systems: building privacy into the net, и . KDD, стр. 627-636. ACM, (2009)Uncheatable Distributed Computations., и . CT-RSA, том 2020 из Lecture Notes in Computer Science, стр. 425-440. Springer, (2001)Domain Extension for Enhanced Target Collision-Resistant Hash Functions.. FSE, том 6147 из Lecture Notes in Computer Science, стр. 153-167. Springer, (2010)Hash Functions: From Merkle-Damgård to Shoup.. EUROCRYPT, том 2045 из Lecture Notes in Computer Science, стр. 166-181. Springer, (2001)(Not So) Random Shuffles of RC4.. CRYPTO, том 2442 из Lecture Notes in Computer Science, стр. 304-319. Springer, (2002)