Cheng Hong

Artificial Intelligence · with Ding · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it
• Privacy & Security — 61 citations into it
• Computational Theory and Mathematics — 12 citations into it
• Algorithms & Complexity — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Cheng Hong trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Craig Gentry
2. Alex Krizhevsky
3. Yehuda Lindell
4. Oded Goldreich
5. Cynthia Dwork
6. Raef Bassily
7. Frank McSherry
8. Tianhao Wang
9. Shai Halevi
10. Nigel P. Smart

Papers from Cheng Hong's perspective

Ordered by how strongly the researchers Cheng Hong trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Learning Multiple Layers of Features from Tiny Images
3. Calibrating Noise to Sensitivity in Private Data Analysis
4. ImageNet classification with deep convolutional neural networks
5. Gradient-based learning applied to document recognition
6. Densely Connected Convolutional Networks
7. Our Data, Ourselves: Privacy Via Distributed Noise Generation
8. The Algorithmic Foundations of Differential Privacy
9. Deep Learning with Differential Privacy
10. Differential Privacy

Their papers

• Cheetah: Lean and Fast Secure Two-Party Deep Neural Network Inference (2022) — 315 citations
• Answering Multi-Dimensional Analytical Queries under Local Differential Privacy (2019) — 109 citations
• Improving utility and security of the shuffler-based differential privacy (2019) — 40 citations