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김미란
Kim, Miran(2020-08-31 ~ 2022-09-01)

김미란

컴퓨터공학과

  • Applied Cryptography Lab.
  • Website
Research Interests
Cryptology;Homomorphic Encryption;Multi-Party Computation;Secure Genome Analysis;Privacy-Preserving Artificial Intelligence;암호론;동형암호;다자간 연산;안전한 유전자 분석;안전한 인공지능
Lab Description
Our main field of research is secure computation, which aims to develop advanced cryptographic primitives to protect sensitive data of individuals. In particular, we are interested in "Homomorphic Encryption" which is an encryption scheme that allows for operations on encrypted inputs without decryption. Currently, we are working on the development of privacy-preserving protocols in a wide range of applications such as genome analysis and machine learning (action/face/voice recognition).
Researcher ID Research Gate ORCiD Scopus Google Scholar
Previous
Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation File File

Kim, Miran , Harmanci, Arif Ozgun , Bossuat, Jean-Philippe , Carpov, Sergiu , Cheon, Jung Hee , Chillotti, Ilaria , Cho, Wonhee , Froelicher, David , Gama, Nicolas , Georgieva, Mariya , et al

Article Issue Date2021-12 View118
A secure system for genomics clinical decision support File

Karimi, Seemeen , Jiang, Xiaoqian , Dolin, Robert H. , Kim, Miran , Boxwala, Aziz

Article Issue Date2020-12 View96
SCOR: A secure international informatics infrastructure to investigate COVID-19 File File

Raisaro, J L , Marino, Francesco , Troncoso-Pastoriza, Juan , Beau-Lejdstrom, Raphaelle , Bellazzi, Riccardo , Murphy, Robert , Bernstam, Elmer V , Wang, Henry , Bucalo, Mauro , Chen, Yong , et al

Article Issue Date2020-11 View108
Semi-Parallel logistic regression for GWAS on encrypted data File File

Kim, Miran , Song, Yongsoo , Li, Baiyu , Micciancio, Daniele

Article Issue Date2020-07 View120
Secure and Differentially Private Logistic Regression for Horizontally Distributed Data File File

Kim, Miran , Lee, Junghye , Ohno-Machado, Lucila , Jiang, Xiaoqian

Article Issue Date2020-01 View126
SecureLR: Secure Logistic Regression Model via a Hybrid Cryptographic Protocol File

Jiang, Yichen , Hamer, Jenny , Wang, Chenghong , Jiang, Xiaoqian , Kim, Miran , Song, Yongsoo , Xia, Yuhou , Mohammed, Noman , Sadat, Md Nazmus , Wang, Shuang

Article Issue Date2019-01 View98
Logistic regression model training based on the approximate homomorphic encryption File File

Kim, Andrey , Song, Yongsoo , Kim, Miran , Lee, Keewoo , Cheon, Jung Hee

Article Issue Date2018-10 View116
Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation File File

Kim, Miran , Song, Yongsoo , Wang, Shuang , Xia, Yuhou , Jiang, Xiaoqian

Article Issue Date2018-04 View105
Secure searching of biomarkers through hybrid homomorphic encryption scheme File File

Kim, Miran , Song, Yongsoo , Cheon, Jung Hee

Article Issue Date2017-07 View96
Optimized Search-and-Compute Circuits and Their Application to Query Evaluation on Encrypted Data File

Cheon, Jung Hee , Kim, Miran , Kim, Myungsun

Article Issue Date2016-01 View114
HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS File

Wang, Shuang , Zhang, Yuchen , Dai, Wenrui , Lauter, Kristin , Kim, Miran , Tang, Yuzhe , Xiong, Hongkai , Jiang, Xiaoqian

Article Issue Date2016-01 View148
Private genome analysis through homomorphic encryption File File

Kim, Miran , Kristin Lauter

Article Issue Date2015-12 View85
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