Eric Chan-Tin

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it
• Privacy & Security — 12 citations into it
• Computer Networks and Communications — 11 citations into it

Whom Eric Chan-Tin trusts overall

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

1. Ilya Mironov
2. Shai Shalev-Shwartz

Papers from Eric Chan-Tin's perspective

Ordered by how strongly the researchers Eric Chan-Tin trusts endorse each paper. Same corpus, a different order for every researcher.

1. Calibrating Noise to Sensitivity in Private Data Analysis
2. Differentially Private Empirical Risk Minimization
3. Deep Learning with Differential Privacy
4. Membership Inference Attacks Against Machine Learning Models
5. The Algorithmic Foundations of Differential Privacy
6. Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds
7. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
8. Robust De-anonymization of Large Sparse Datasets
9. Rényi Differential Privacy
10. Stochastic Approximation approach to Stochastic Programming

Their papers

• DP-ADMM: ADMM-Based Distributed Learning With Differential Privacy (2018) — 236 citations