Miran Kim
Artificial Intelligence · 2015–2019
A researcher in the Science corpus: 8 papers; trusts 50 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 132 citations into it; #8,880 of 35,345 by trust here, #8,893 by citations
- Computational Theory and Mathematics — 68 citations into it; #3,684 of 12,182 by trust here, #2,931 by citations
- Privacy & Security — 51 citations into it
- Information Systems — 22 citations into it; #2,171 of 8,771 by trust here, #2,471 by citations
- Computer Vision and Pattern Recognition — 18 citations into it; #19,669 of 21,575 by trust here, #18,266 by citations
- Algorithms & Complexity — 7 citations into it
- Electrical and Electronic Engineering — 6 citations into it
- ML Systems & Hardware — 6 citations into it
Whom Miran Kim trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Craig Gentry
- Shai Halevi
- Kristin E. Lauter
- Zvika Brakerski
- Joppe W. Bos
- Nigel P. Smart
- Vinod Vaikuntanathan
- Michael Naehrig
- Chris Peikert
- Adriana López-Alt
Who trusts Miran Kim overall
- Hao Chen
- M. Sadegh Riazi
- Andrey Kim
- Yongsoo Song
- Wei Dai
- Kim Laine
- A. Al Badawi
- Qiang Yang
- Mohammad Yaghini
- Jung Hee Cheon
Papers from Miran Kim's perspective
Ordered by how strongly the researchers Miran Kim trusts endorse each paper. Same corpus, a different order for every researcher.
- Fully homomorphic encryption using ideal lattices
- On Ideal Lattices and Learning with Errors over Rings
- Fully Homomorphic Encryption from Ring-LWE and Security for Key Dependent Messages
- A fully homomorphic encryption scheme
- Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
- Efficient Fully Homomorphic Encryption from (Standard) LWE
- Better Key Sizes (and Attacks) for LWE-Based Encryption
- Gradient-based learning applied to document recognition
- ImageNet classification with deep convolutional neural networks
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Bootstrapping for Approximate Homomorphic Encryption (2018) — 436 citations
- Homomorphic Encryption for Arithmetic of Approximate Numbers (2017) — 2,700 citations
- Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference (2019) — 240 citations
- Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation (2018) — 296 citations
- Secure Outsourced Matrix Computation and Application to Neural Networks (2018) — 362 citations
- A Full RNS Variant of Approximate Homomorphic Encryption (2018) — 476 citations
- Logistic regression model training based on the approximate homomorphic encryption (2018) — 242 citations
- Private genome analysis through homomorphic encryption (2015) — 163 citations