Guoxing Chen
Artificial Intelligence · 2015–2018
A researcher in the Science corpus: 4 papers; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 112 citations into it
- Signal Processing — 55 citations into it
- Hardware and Architecture — 22 citations into it
- Privacy & Security — 17 citations into it
- Optimization & Training — 13 citations into it
- Computer Vision and Pattern Recognition — 8 citations into it
Whom Guoxing Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yuval Yarom
- Eran Tromer
- Marcus Hähnel
- Hovav Shacham
- L. Eon Bottou
- Marcus Peinado
- Dag Arne Osvik
- Weidong Cui
- Yinqian Zhang
- Zhenghong Wang
Who trusts Guoxing Chen overall
Papers from Guoxing Chen's perspective
Ordered by how strongly the researchers Guoxing Chen trusts endorse each paper. Same corpus, a different order for every researcher.
- Shielding Applications from an Untrusted Cloud with Haven
- Controlled-Channel Attacks: Deterministic Side Channels for Untrusted Operating Systems
- Cache Attacks and Countermeasures: The Case of AES (The Cryptographer's Track at RSA Conference)
- VC3: Trustworthy Data Analytics in the Cloud Using SGX
- This paper is included in the Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16).
- Last-Level Cache Side-Channel Attacks are Practical (IEEE Symposium on Security and Privacy)
- Innovative instructions and software model for isolated execution
- Software Grand Exposure: SGX Cache Attacks Are Practical
- Learning Multiple Layers of Features from Tiny Images
- T-SGX: Eradicating Controlled-Channel Attacks Against Enclave Programs
Their papers
- SgxPectre: Stealing Intel Secrets from SGX Enclaves Via Speculative Execution (2018) — 383 citations
- SgxPectre Attacks: Leaking Enclave Secrets via Speculative Execution (2018) — 138 citations
- Leaky Cauldron on the Dark Land: Understanding Memory Side-Channel Hazards in SGX (2017) — 411 citations
- Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices (2015) — 65 citations