For modulus $Q$ and stddev $\sigma$, [GHS12] suggests that, to achieve 128-bit security, just choose the dimension $N$: $$ N\geq(Q/\sigma)\cdot 33.1 $$

This seems to suggest flexibility to choose smaller $\sigma$ ("as long as it is not too tiny"), but just paying a price on $N$.

Many instantiations seem to favor a fixed $\sigma$ about $3.2$ ([GHS12]). Especially, the homomorphic encryption standard said:

The standard deviation that we use below is chosen as $\sigma = 8/\sqrt{2}\pi \approx 3.2$, which is a value that is used in many libraries in practice and for which no other attacks are known. (Some proposals in the literature suggest even smaller values of $\sigma$.)

There are schemes using a smaller $\sigma$. Frodo is looking at approximating $\sigma\approx1$.

My question:

In my use case, I do need the noise to be very small.

  • Can one do $\sigma\approx0.1$?
  • If not, how small can $\sigma$ be, if one needs to minimize it?
  • Is it because we need to approximate continuous Gaussian, so $\sigma$ can never be too small?

Note that $\sigma\approx 0.1$ seems unreasonable, since, with a high possibility, all sampled values will be zero (by making it an integer). And [GHS12] mentions explicitly that the $N\geq(Q/\sigma)\cdot 33.1$ check does not apply to "too tiny $\sigma$", although the paper did not explicitly mention what is considered "too tiny".

Yet, the LWE estimator seems "okay" with $\sigma\approx0.1$. I guess small $\sigma$ is beyond the scope of the LWE estimator?

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    $\begingroup$ I am not too familiar with the LWE estimator, but it seems that it has a function for evaluating the "Small $\sigma$" attack (the Arora-Ge attack). Do you get different results when you call that directly? $\endgroup$
    – Mark Schultz-Wu
    Feb 12, 2021 at 20:27
  • $\begingroup$ Great points! Just notice that by default, the LWE estimator did not run 'mitm', 'arora-gb', 'bkw`. They are likely heavy---as I am still running and waiting for the results. Will keep you posted. $\endgroup$ Feb 12, 2021 at 20:33
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    $\begingroup$ An update: the estimator for the Arora-Ge attack is still running. Have the feeling that this one is going to take a fairly long time. Still, I will keep you posted when more results come out. $\endgroup$ Feb 12, 2021 at 21:37
  • $\begingroup$ Update: For small parameters (q~20000), the estimator allows smaller stddev like 1 and 0.8 and does display a security loss between them. However, for q that is pretty large in my case (q~2^64), the estimator cannot compute for small stddev ("insufficient samples" or something like that), and computing for stddev=4 is still running. I feel I would need to find some approximation algorithms/simplified models for Arora-Ge attack. $\endgroup$ Feb 15, 2021 at 22:42

1 Answer 1


Per Mark's suggestion, I looked into the "hidden" tests in lwe-estimator and read a few papers. I summarized my findings as an answer here:

  • Arora-Ge attack, and the improved version using Grobner bases, work better when $q$ is small, but it starts to be impractical once the number of samples $n$ and $q$ is very large, e.g., $q=2^{64}$. This seems to be due to the time needed for linearization.

  • Another hidden test, MITM (Section 5.1 in https://eprint.iacr.org/2015/046.pdf), can be useful here---MITM considers $\alpha q$, and it is important that $\alpha$ cannot be way too small. Reflected in lwe-estimator, this would disallow one to use $\sigma\approx0.001$.

Still, more discussion is welcome. For my own use case, it seems that $\sigma=1$ or $\sigma\approx0.8$ may be generally okay since my $q$ is large.


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