Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Yuhou Xia"'
Publikováno v:
BMC Medical Genomics, Vol 10, Iss S2, Pp 1-14 (2017)
Abstract Background One of the tasks in the iDASH Secure Genome Analysis Competition in 2016 was to demonstrate the feasibility of privacy-preserving queries on homomorphically encrypted genomic data. More precisely, given a list of up to 100,000 mut
Externí odkaz:
https://doaj.org/article/ca4b807bbd004d6d9df2301319532def
Autor:
Chenghong Wang, Nazmus Sadat, Jenny Hamer, Shuang Wang, Miran Kim, Yuhou Xia, Xiaoqian Jiang, Yongsoo Song, Yichen Jiang, Noman Mohammed
Publikováno v:
IEEE/ACM Transactions on Computational Biology and Bioinformatics. 16:113-123
Machine learning applications are intensively utilized in various science fields, and increasingly the biomedical and healthcare sector. Applying predictive modeling to biomedical data introduces privacy and security concerns requiring additional pro
Autor:
Yuhou Xia
Publikováno v:
Mathematische Annalen. 374:1953-1986
Let $$\pi $$ be a polarizable, regular algebraic, cuspidal automorphic representation of $$\text { GL }_n(\mathbb {A}_F)$$ , where F is an imaginary CM field and $$n \le 6$$ . We show that there is a Dirichlet density 1 set $$\mathfrak {L}$$ of ratio
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319769523
CT-RSA
CT-RSA
In most RLWE-based homomorphic encryption schemes the native plaintext elements are polynomials in a ring \(\mathbb {Z}_t[x]/(x^n+1)\), where n is a power of 2, and t an integer modulus. For performing integer or rational number arithmetic, one typic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::16ce758c81c6f0ca89bc30c9c1f091c6
https://doi.org/10.1007/978-3-319-76953-0_7
https://doi.org/10.1007/978-3-319-76953-0_7
BACKGROUND Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain anal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b8d84147ba277d0644422a0ec645afef
https://doi.org/10.2196/preprints.8805
https://doi.org/10.2196/preprints.8805
Publikováno v:
JMIR Medical Informatics
Background: Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain ana
Publikováno v:
BMC Medical Genomics
BMC Medical Genomics, Vol 10, Iss S2, Pp 1-14 (2017)
BMC Medical Genomics, Vol 10, Iss S2, Pp 1-14 (2017)
Background One of the tasks in the iDASH Secure Genome Analysis Competition in 2016 was to demonstrate the feasibility of privacy-preserving queries on homomorphically encrypted genomic data. More precisely, given a list of up to 100,000 mutations, t
Publikováno v:
Physical Review A. 84
We present rigorous performance bounds for the quadratic dynamical decoupling (QDD) pulse sequence which protects a qubit from general decoherence, and for its nested generalization to an arbitrary number of qubits. Our bounds apply under the assumpt
Publikováno v:
BMC Medical Genomics; 7/26/2017, Vol. 10, p1-14, 14p