Crates.io | userspace-rng |
lib.rs | userspace-rng |
version | 1.0.3 |
source | src |
created_at | 2022-11-28 15:32:13.504974 |
updated_at | 2023-02-05 10:14:39.629869 |
description | provides secure randomness with entropy generated in userspace |
homepage | |
repository | https://github.com/DavidVorick/userspace-rng |
max_upload_size | |
id | 724515 |
size | 31,710 |
userspace-random is a rust crate that is intended to provde secure entropy to
the caller even if the operating system entropy is not secure.
userspace_rng::Csprng
implements rand_core::CryptoRng
and
rand_core::RngCore
.
Usage:
// Generate an ed25519 key.
let mut rng = userspace_rng::Csprng{};
let keypair = ed25519_dalek::Keypair::generate(&mut rng);
// Generate 32 bytes of randomness.
let rand_data = userspace_rng::random256();
Modern operating systems like Linux, Mac, and Windows will all provide reliable entropy, however more niche operating systems and environments often will provide broken RNGs to the user. This concern is especially relevant to IoT and embedded hardware, where the engineers are deliberately taking as many shortcuts as they can to trim down the size and overhead of the operating system and hardware. The result may be an unintentionally compromised operating system RNG which can put users at risk.
This worry has precedent. When Android was newer, a broken RNG in Android put user funds at risk: https://bitcoin.org/en/alert/2013-08-11-android
To protect users against insecure hardware, we developed a library that generates reliable entropy in userspace. We have constructed the library to ensure that the randomness can only be compromised if both the operating system RNG is broken and also the assumptions made by our library are incorrect. Had the Android wallets used userspace-random to generate their entropy, user funds likely would have been safe despite the flaw in Android itself.
This library draws entropy from CPU jitter. Specifically, the number of nanoseconds required to complete a cryptographic hash function has a high amount of variance. My own testing suggested that under normal circumstances the variance is around 100ns with a standard deviation of around 40ns. This suggests that using time elapsed during a cryptographic hashing function as a source of entropy will provide between 4 and 6 bits of entropy per measurement.
When the library starts up, it performs 25 milliseconds of hashing, performing a strict minimum of 512 hashing operations total. This theoretically provides more than 2000 bits of entropy, which means there is a comfortable safety margin provided by the library. Even hardware that is more stable and reliable should be producing at least 1/4 bits of entropy per hash operation owing to the natural physical limitations of CPUs.
To give some brief intuition: a CPU is a physical device that consumes electricity and produces heat. The speed at which a CPU operates (at least, with our current technology) is surprisingly dependent on factors like voltage and temperature. Further, these factors tend to vary rapidly as a CPU performs computations. Empirical measurements demonstrate that the variance is sufficiently significant to cause material variance in the total execution time of a cryptographic hash operation. Other factors such as background activity by the operating system can also play a role.
For fun, this library also incorporates some ideas from the fortuna paper to protect users against an adversary that has the ability to use side channels to compromise the user entropy. In reality, these extra choices are probably not necessary to protect against real-world attackers. Fortuna was designed with a very different threat model in mind than the one we face with this library.
Fortuna paper: https://www.schneier.com/wp-content/uploads/2015/12/fortuna.pdf