Wonhee Cho
Seoul National University
21 Papers
18 Citations
Wonhee Cho is an academic researcher from Seoul National University. The author has contributed to research in topics: Computer science & Encryption. The author has an hindex of 4, co-authored 13 publications.
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Papers
Hardware Architecture of a Number Theoretic Transform for a Bootstrappable RNS-based Homomorphic Encryption Scheme
Sunwoong Kim,Keewoo Lee,Wonhee Cho,Yujin Nam,Jung Hee Cheon,Rob A. Rutenbar +5 more
- 03 May 2020
TL;DR: This paper suggests practical bootstrappable parameters, specifically for an established residue number system (RNS)based HE scheme, and applies them to the NTT hardware design, which achieves a $118 \times faster processing speed than a software implementation.
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Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation.
Miran Kim,Arif Harmanci,Jean-Philippe Bossuat,Sergiu Carpov,Jung Hee Cheon,Ilaria Chillotti,Wonhee Cho,David Froelicher,Nicolas Gama,Mariya Georgieva,Seungwan Hong,Jean-Pierre Hubaux,Duhyeong Kim,Kristin E. Lauter,Yiping Ma,Lucila Ohno-Machado,Heidi J. Sofia,Yongha Son,Yongsoo Song,Juan Ramón Troncoso-Pastoriza,Xiaoqian Jiang +20 more
TL;DR: In this article, the authors developed secure genotype imputation using efficient homomorphic encryption (HE) techniques, where the genotype data are secure while it is in transit, at rest, and in analysis.
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FPGA-based Accelerators of Fully Pipelined Modular Multipliers for Homomorphic Encryption
Sunwoong Kim,Keewoo Lee,Wonhee Cho,Jung Hee Cheon,Rob A. Rutenbar +4 more
- 01 Dec 2019
TL;DR: A set of novel FPGA-based accelerators for fully pipelined ModMults to address the speed problem of modular multiplication, and the proposed Barrett and Shoup ModMult designs show the highest throughput/DSP value although precomputation required in the ShOUP ModMult design is not used.
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Secure tumor classification by shallow neural network using homomorphic encryption
Seungwan Hong,Jai Hyun Park,Wonhee Cho,Hyeongmin Choe,Jung Hee Cheon +4 more
TL;DR: A secure multi-label tumor classification method using HE to ensure privacy during all the computations of the model inference process was proposed in this article . But the method requires a large-scale genetic data set.
META-BTS: Bootstrapping Precision Beyond the Limit
Youngjin Bae,Jung Hee Cheon,Wonhee Cho,Jaehyung Kim,Tae Joon Kim +4 more
TL;DR: This paper proposes a new bootstrapping algorithm of the Cheon-Kim- Kim-Song (CKKS) scheme to use a known bootstrapped algorithm repeatedly, so called Meta-BTS, which overcomes the precision limitation given by the rescale operation.
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