Linshan Jiang

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it
• Privacy & Security — 14 citations into it

Whom Linshan Jiang trusts overall

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

1. Juan Benet
2. Cynthia Dwork
3. Yossi Gilad

Papers from Linshan Jiang's perspective

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

1. Calibrating Noise to Sensitivity in Private Data Analysis
2. Communication-Efficient Learning of Deep Networks from Decentralized Data
3. Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning (Conference on Computer and Communications Security)
4. Federated Learning: Strategies for Improving Communication Efficiency
5. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
6. Gradient-based learning applied to document recognition
7. Exploiting Unintended Feature Leakage in Collaborative Learning
8. ImageNet classification with deep convolutional neural networks
9. Our Data, Ourselves: Privacy Via Distributed Noise Generation
10. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)

Their papers

• Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System (2019) — 113 citations
• Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2019) — 508 citations