Lessons Learned from Blockchain Applications of Trusted Execution Environments and Implications for Future Research
March 23, 2022 ยท Declared Dead ยท ๐ HASP@MICRO
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Rabimba Karanjai, Lei Xu, Lin Chen, Fengwei Zhang, Zhimin Gao, Weidong Shi
arXiv ID
2203.12724
Category
cs.CR: Cryptography & Security
Citations
12
Venue
HASP@MICRO
Last Checked
3 months ago
Abstract
Modern computer systems tend to rely on large trusted computing bases (TCBs) for operations. To address the TCB bloating problem, hardware vendors have developed mechanisms to enable or facilitate the creation of a trusted execution environment (TEE) in which critical software applications can execute securely in an isolated environment. Even under the circumstance that a host OS is compromised by an adversary, key security properties such as confidentiality and integrity of the software inside the TEEs can be guaranteed. The promise of integrity and security has driven developers to adopt it for use cases involving access control, PKS, IoT among other things. Among these applications include blockchain-related use cases. The usage of the TEEs doesn't come without its own implementation challenges and potential pitfalls. In this paper, we examine the assumptions, security models, and operational environments of the proposed TEE use cases of blockchain-based applications. The exercise and analysis help the hardware TEE research community to identify some open challenges and opportunities for research and rethink the design of hardware TEEs in general.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Cryptography & Security
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Membership Inference Attacks against Machine Learning Models
R.I.P.
๐ป
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
๐ป
Ghosted
Practical Black-Box Attacks against Machine Learning
R.I.P.
๐ป
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
๐ป
Ghosted
Extracting Training Data from Large Language Models
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted