Yongsoo Song
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 8 papers; trusts 43 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 116 citations into it; #9,130 of 35,345 by trust here, #9,760 by citations
- Computational Theory and Mathematics — 57 citations into it; #3,683 of 12,182 by trust here, #2,931 by citations
- Privacy & Security — 47 citations into it
- Information Systems — 20 citations into it; #2,170 of 8,771 by trust here, #2,471 by citations
- Computer Vision and Pattern Recognition — 16 citations into it; #19,668 of 21,575 by trust here, #18,266 by citations
- Algorithms & Complexity — 6 citations into it
- ML Systems & Hardware — 6 citations into it
- Optimization & Training — 6 citations into it
Whom Yongsoo Song trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Craig Gentry
- Shai Halevi
- Zvika Brakerski
- Jung Hee Cheon
- Nigel P. Smart
- Miran Kim
- Vinod Vaikuntanathan
- Payman Mohassel
- Kristin E. Lauter
- Andrey Kim
Who trusts Yongsoo Song overall
- Hao Chen
- M. Sadegh Riazi
- Miran Kim
- Kim Laine
- Wei Dai
- Andrey Kim
- A. Al Badawi
- Zhicong Huang
- Jung Hee Cheon
- Jinhyun So
Papers from Yongsoo Song's perspective
Ordered by how strongly the researchers Yongsoo Song 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
- Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
- A fully homomorphic encryption scheme
- Fully Homomorphic Encryption from Ring-LWE and Security for Key Dependent Messages
- Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based
- SecureML: A System for Scalable Privacy-Preserving Machine Learning
- Efficient Fully Homomorphic Encryption from (Standard) LWE
- Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP
- Better Key Sizes (and Attacks) for LWE-Based Encryption
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
- APPLICATIONS OF HOMOMORPHIC ENCRYPTION (2017)