Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Learning Parity with Noise"'
Autor:
Shengfeng Xu, Xiangxue Li
Publikováno v:
IEEE Access, Vol 9, Pp 13683-13690 (2021)
Key Encapsulation Mechanism (KEM) is a foundational cryptography primitive, which can provide secure symmetric cryptographic key material for transmission by using public key algorithms. Until now, many Chosen-Ciphertext (IND-CCA) secure KEM schemes
Externí odkaz:
https://doaj.org/article/94ae166494354b6590e6a1cc4b50e2f3
Akademický článek
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Autor:
Davide Bellizia, Clément Hoffmann, Dina Kamel, Hanlin Liu, Pierrick Méaux, François-Xavier Standaert, Yu Yu
Publikováno v:
Transactions on Cryptographic Hardware and Embedded Systems, Vol 2021, Iss 3 (2021)
Hard learning problems are important building blocks for the design of various cryptographic functionalities such as authentication protocols and post-quantum public key encryption. The standard implementations of such schemes add some controlled err
Externí odkaz:
https://doaj.org/article/9f5b2c12ef5642fa8ffc519bda795862
Akademický článek
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Akademický článek
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Autor:
Dina Kamel, Clément Hoffmann, Hanlin Liu, Yu Yu, Davide Bellizia, François-Xavier Standaert, Pierrick Méaux
Publikováno v:
IACR Transactions on Cryptographic Hardware and Embedded Systems, Vol. 2021, no.3, p. 390-417 (2021)
Transactions on Cryptographic Hardware and Embedded Systems, Vol 2021, Iss 3 (2021)
Transactions on Cryptographic Hardware and Embedded Systems, Vol 2021, Iss 3 (2021)
Hard learning problems are important building blocks for the design of various cryptographic functionalities such as authentication protocols and post-quantum public key encryption. The standard implementations of such schemes add some controlled err
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8aa791f85a4d302348980efb81d61168
https://doi.org/10.46586/tches.v2021.i3.390-417
https://doi.org/10.46586/tches.v2021.i3.390-417
Autor:
Xiangxue Li, Shengfeng Xu
Publikováno v:
IEEE Access, Vol 9, Pp 13683-13690 (2021)
Key Encapsulation Mechanism (KEM) is a foundational cryptography primitive, which can provide secure symmetric cryptographic key material for transmission by using public key algorithms. Until now, many Chosen-Ciphertext (IND-CCA) secure KEM schemes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::538dcb9d9f0294abb0474b0675fe46fc
https://ieeexplore.ieee.org/document/9320474/
https://ieeexplore.ieee.org/document/9320474/
Autor:
Chenglu Jin, Charles Herder, Ling Ren, Phuong Ha Nguyen, Benjamin Fuller, Srinivas Devadas, Marten van Dijk
Publikováno v:
Cryptography, Vol 1, Iss 3, p 23 (2017)
Herder et al. (IEEE Transactions on Dependable and Secure Computing, 2017) designed a new computational fuzzy extractor and physical unclonable function (PUF) challenge-response protocol based on the Learning Parity with Noise (LPN) problem. The prot
Externí odkaz:
https://doaj.org/article/2d26dc267c664d50bb7394bc69c90fac
Autor:
Geoffroy Couteau, Pierre Meyer
Publikováno v:
EUROCRYPT 2021-Annual International Conference on the Theory and Applications of Cryptographic Techniques
EUROCRYPT 2021-Annual International Conference on the Theory and Applications of Cryptographic Techniques, Oct 2021, Zagreb, Croatia. pp.842-870, ⟨10.1007/978-3-030-77886-6_29⟩
Lecture Notes in Computer Science ISBN: 9783030778859
EUROCRYPT (2)
EUROCRYPT 2021-Annual International Conference on the Theory and Applications of Cryptographic Techniques, Oct 2021, Zagreb, Croatia. pp.842-870, ⟨10.1007/978-3-030-77886-6_29⟩
Lecture Notes in Computer Science ISBN: 9783030778859
EUROCRYPT (2)
International audience; In this work we introduce a new (circuit-dependent) homomorphic secret sharing (HSS) scheme for any log / log log-local circuit, with communication proportional only to the width of the circuit and polynomial computation, whic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fc316b301d63c1d4cb9425f1c28c720
https://hal.archives-ouvertes.fr/hal-03374180/file/2021-943.pdf
https://hal.archives-ouvertes.fr/hal-03374180/file/2021-943.pdf
In this work, we show, for the well-studied problem of learning parity under noise, where a learner tries to learn $x=(x_1,\ldots,x_n) \in \{0,1\}^n$ from a stream of random linear equations over $\mathrm{F}_2$ that are correct with probability $\fra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34616274e81e00ba3f8dd0d9cb2c6408
http://arxiv.org/abs/2107.02320
http://arxiv.org/abs/2107.02320