Leveraging Intel SGX to Create a Nondisclosure Cryptographic library
May 12, 2017 ยท Entered Twilight ยท ๐ arXiv.org
"Last commit was 7.0 years ago (โฅ5 year threshold)"
Evidence collected by the PWNC Scanner
Repo contents: App, Common, Enclave.token, Enclave, Makefile, README.md, gitty.sh, gutenberg
Authors
Mohammad Hasanzadeh Mofrad, Adam Lee
arXiv ID
1705.04706
Category
cs.CR: Cryptography & Security
Citations
11
Venue
arXiv.org
Repository
https://github.com/hmofrad/CryptoEnclave
โญ 7
Last Checked
1 month ago
Abstract
Enforcing integrity and confidentiality of users' application code and data is a challenging mission that any software developer working on an online production grade service is facing. Since cryptology is not a widely understood subject, people on the cutting edge of research and industry are always seeking for new technologies to naturally expand the security of their programs and systems. Intel Software Guard Extension (Intel SGX) is an Intel technology for developers who are looking to protect their software binaries from plausible attacks using hardware instructions. The Intel SGX puts sensitive code and data into CPU-hardened protected regions called enclaves. In this project we leverage the Intel SGX to produce a secure cryptographic library which keeps the generated keys inside an enclave restricting use and dissemination of confidential cryptographic keys. Using enclaves to store the keys we maintain a small Trusted Computing Base (TCB) where we also perform computation on temporary buffers to and from untrusted application code. As a proof of concept, we implemented hashes and symmetric encryption algorithms inside the enclave where we stored hashes, Initialization Vectors (IVs) and random keys and open sourced the code (https://github.com/hmofrad/CryptoEnclave).
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