Payman Mohassel
Artificial Intelligence · 2006–2019
A researcher in the Science corpus: 5 papers; trusts 69 researchers and is trusted by 154 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 89 citations into it; #1,657 of 35,345 by trust here, #4,205 by citations
- Privacy & Security — 58 citations into it; #30 of 709 by trust here, #98 by citations
- Computational Theory and Mathematics — 24 citations into it; #729 of 12,182 by trust here, #1,463 by citations
- Safety, Robustness & Interpretability — 16 citations into it
- Information Systems — 14 citations into it
- Computer Networks and Communications — 12 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
- Electrical and Electronic Engineering — 8 citations into it
Whom Payman Mohassel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ran Canetti
- Jaideep Vaidya
- Wenliang Du
- Mihir Bellare
- Hwanjo Yu
- Vladimir Kolesnikov
- Thomas Schneider
- Pascal Paillier
- Valeria Nikolaenko
- Yehuda Lindell
Who trusts Payman Mohassel overall
- Sameer Wagh
- Qiang Yang
- Yehuda Lindell
- Yang Liu
- Divya Gupta
- Nishant Kumar
- Arpita Patra
- M. Sadegh Riazi
- Assi Barak
- Nishanth Chandran
Papers from Payman Mohassel's perspective
Ordered by how strongly the researchers Payman Mohassel trusts endorse each paper. Same corpus, a different order for every researcher.
- Learning Multiple Layers of Features from Tiny Images
- Protocols for secure computations
- Privacy Preserving Data Mining
- Fair exchange with a semi-trusted third party (extended abstract)
- Gradient-based learning applied to document recognition
- Fairplay - Secure Two-Party Computation System
- Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Privacy preserving auctions and mechanism design
Their papers
- SecureML: A System for Scalable Privacy-Preserving Machine Learning (2017) — 2,156 citations
- Efficiency Tradeoffs for Malicious Two-Party Computation (2006) — 162 citations
- Fast and Secure Three-party Computation: The Garbled Circuit Approach (2015) — 123 citations
- ABY3: A Mixed Protocol Framework for Machine Learning (2018) — 920 citations
- CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning (2019) — 133 citations