Wei Dai
Artificial Intelligence · with Laine · 2014–2019
A researcher in the Science corpus: 5 papers; trusts 75 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 153 citations into it; #11,345 of 35,345 by trust here, #17,776 by citations
- Information Systems — 70 citations into it; #3,866 of 8,771 by trust here, #5,942 by citations
- Computational Theory and Mathematics — 50 citations into it
- Privacy & Security — 30 citations into it
- Computer Vision and Pattern Recognition — 17 citations into it
- Hardware and Architecture — 7 citations into it
Whom Wei Dai trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Craig Gentry
- Shai Halevi
- Zvika Brakerski
- Nigel P. Smart
- Wei Wang
- Yarkin Doröz
- Vinod Vaikuntanathan
- Adriana López-Alt
- Damien Stehlé
- Frederik Vercauteren
Who trusts Wei Dai overall
- M. Sadegh Riazi
- Erdinç Öztürk
- Yarkin Doröz
- Berk Sunar
- Kim Laine
- Erkay Savas
- Blake Pelton
- Brandon Reagen
- Wooseok Choi
- Zhicong Huang
Papers from Wei Dai's perspective
Ordered by how strongly the researchers Wei Dai trusts endorse each paper. Same corpus, a different order for every researcher.
- Fully homomorphic encryption using ideal lattices
- A fully homomorphic encryption scheme
- On Ideal Lattices and Learning with Errors over Rings
- Efficient Fully Homomorphic Encryption from (Standard) LWE
- Fully Homomorphic Encryption from Ring-LWE and Security for Key Dependent Messages
- (Leveled) Fully Homomorphic Encryption without Bootstrapping
- Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP
- On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption
- Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based
- Implementing Gentry's Fully-Homomorphic Encryption Scheme
Their papers
- Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference (2019) — 240 citations
- HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud (2019) — 9 citations
- HEAX: An Architecture for Computing on Encrypted Data (2019) — 278 citations
- cuHE: A Homomorphic Encryption Accelerator Library (2015) — 159 citations
- Accelerating NTRU based homomorphic encryption using GPUs (2014) — 71 citations