5 ms·

I'd like to add "truly random" to > use unique strong passwords per-website. ... use unique, truly random passwords per-website. "Strong" is ambigous and har

by berkes 8y ago

I'd like to add "truly random" to > use unique strong passwords per-website. ... use unique, truly random passwords per-website. "Strong" is ambigous and hard to explain how to do right. Random means, in practice: "don't come up with one yourself, but let a tool generate one for you".

akerl_ 8y ago
As a user, how do I know if a tool is generating a "truly random" password?
ChrisSD 8y ago
To nitpick slightly, I think "truly random" is the wrong phrase here. Computers can't generate such numbers (only pseudo-random). What they can do is generate unpredictable passwords. Unpredictability is the feature we want in a password, whether that happens through "randomness" or something else doesn't matter. Of course computers are far better at coming up with unpredictable passwords than humans are.
- zokier 8y ago
  RDRAND is supposed to generate very true random numbers
  - ChrisSD 8y ago
    Well either a RNG generates true random numbers or it doesn't. There's no room for "very" here. From Intel[0] > With respect to the RNG taxonomy discussed above, the DRNG follows the cascade construction RNG model, using a processor resident entropy source to repeatedly seed a hardware-implemented CSPRNG. Unlike software approaches, it includes a high-quality entropy source implementation that can be sampled quickly to repeatedly seed the CSPRNG with high-quality entropy. So it's a cryptographically secure pseudo random number generator that takes entropy from the processor. It's not a True Random Number Generator. And again, if it does work well for cryptography, it's the unpredictability that matters, not the randomness itself. [0] https://software.intel.com/en-us/articles/intel-digital-random-number-generator-drng-software-implementation-guide https://software.intel.com/en-us/articles/intel-digital-rand...
    - zokier 8y ago
      From the link you provided: > This method of digital random number generation is unique in its approach to true random number generation in that it is implemented in the processor’s hardware > The all-digital Entropy Source (ES), also known as a non-deterministic random bit generator (NRBG), provides a serial stream of entropic data in the form of zeroes and ones. > The ES runs asynchronously on a self-timed circuit and uses thermal noise within the silicon to output a random stream of bits at the rate of 3 GHz What on earth would classify as TRNG if not above? Just fyi, checked, and NIST SP 800-90B defines NRBG as following: > Non-deterministic Random Bit Generator (NRBG): > An RBG that always has access to an entropy source and (when working properly) produces outputs that have full entropy (see SP 800-90C). Also called a true random bit (or number) generator (Contrast with a DRBG).
throwawaymath 8y ago
You're looking for "pseudorandom", not truly random. True randomness is an unnecessary and unrealistic bar to impose for typical cryptographic purposes. Not only is using true randomness more computationally and logistically expensive, it's difficult to implement correctly and safely. If you have a CSPRNG with sufficient pseudorandomness, it's more important to secure and optimize other parts of the system than it is to get stuck in local maxima with randomness fetishization.

by berkes 8y ago

akerl_ 8y ago

As a user, how do I know if a tool is generating a "truly random" password?

ChrisSD 8y ago

To nitpick slightly, I think "truly random" is the wrong phrase here. Computers can't generate such numbers (only pseudo-random). What they can do is generate unpredictable passwords. Unpredictability is the feature we want in a password, whether that happens through "randomness" or something else doesn't matter. Of course computers are far better at coming up with unpredictable passwords than humans are.

zokier 8y ago
RDRAND is supposed to generate very true random numbers
- ChrisSD 8y ago
  Well either a RNG generates true random numbers or it doesn't. There's no room for "very" here. From Intel[0] > With respect to the RNG taxonomy discussed above, the DRNG follows the cascade construction RNG model, using a processor resident entropy source to repeatedly seed a hardware-implemented CSPRNG. Unlike software approaches, it includes a high-quality entropy source implementation that can be sampled quickly to repeatedly seed the CSPRNG with high-quality entropy. So it's a cryptographically secure pseudo random number generator that takes entropy from the processor. It's not a True Random Number Generator. And again, if it does work well for cryptography, it's the unpredictability that matters, not the randomness itself. [0] https://software.intel.com/en-us/articles/intel-digital-random-number-generator-drng-software-implementation-guide https://software.intel.com/en-us/articles/intel-digital-rand...
  - zokier 8y ago
    From the link you provided: > This method of digital random number generation is unique in its approach to true random number generation in that it is implemented in the processor’s hardware > The all-digital Entropy Source (ES), also known as a non-deterministic random bit generator (NRBG), provides a serial stream of entropic data in the form of zeroes and ones. > The ES runs asynchronously on a self-timed circuit and uses thermal noise within the silicon to output a random stream of bits at the rate of 3 GHz What on earth would classify as TRNG if not above? Just fyi, checked, and NIST SP 800-90B defines NRBG as following: > Non-deterministic Random Bit Generator (NRBG): > An RBG that always has access to an entropy source and (when working properly) produces outputs that have full entropy (see SP 800-90C). Also called a true random bit (or number) generator (Contrast with a DRBG).

throwawaymath 8y ago

You're looking for "pseudorandom", not truly random. True randomness is an unnecessary and unrealistic bar to impose for typical cryptographic purposes. Not only is using true randomness more computationally and logistically expensive, it's difficult to implement correctly and safely. If you have a CSPRNG with sufficient pseudorandomness, it's more important to secure and optimize other parts of the system than it is to get stuck in local maxima with randomness fetishization.