Security Estimates for the Learning with Errors Problem
The main intend of this estimator is to give designers an easy way to choose parameters resisting known attacks and to enable cryptanalysts to compare their results and ideas with other techniques known in the literature.
sage: load("estimator.py") sage: n, alpha, q = Param.Regev(128) sage: costs = estimate_lwe(n, alpha, q) usvp: rop: ≈2^51.1, red: ≈2^51.1, δ_0: 1.009214, β: 102, d: 357, m: 610 dec: rop: ≈2^56.8, m: 235, red: ≈2^56.8, δ_0: 1.009311, β: 99, d: 363, babai: ≈2^42.2, babai_op: ≈2^57.3, repeat: 146, ε: 0.031250 dual: rop: ≈2^74.7, m: 376, red: ≈2^74.7, δ_0: 1.008810, β: 111, d: 376, |v|: 736.521, repeat: ≈2^19.0, ε: 0.003906
At present the following algorithms are covered by this estimator.
- meet-in-the-middle exhaustive search
- Coded-BKW [C:GuoJohSta15]
- dual-lattice attack and small/sparse secret variant [EC:Albrecht17]
- lattice-reduction + enumeration [RSA:LinPei11]
- primal attack via uSVP [ICISC:AlbFitGop13,ACISP:BaiGal14]
- Arora-Ge algorithm [ICALP:AroGe11] using Gröbner bases [EPRINT:ACFP14]
Above, we use cryptobib-style bibtex keys as references.
This code is evolving, new results are added and bugs are fixed. Hence, estimations from earlier versions might not match current estimations. This is annoying but unavoidable at present. We recommend to also state the commit that was used when referencing this project.
We also encourage authors to let us know if their paper uses this code. In particular, we thrive to tag commits with those cryptobib ePrint references that use it. For example, this commit corresponds to this ePrint entry.
Our intent is for this estimator to be maintained by the research community. For example, we encourage algorithm designers to add their own algorithms to this estimator and we are happy to help with that process.
More generally, all contributions such as bugfixes, documentation and tests are welcome. Please go ahead and submit your pull requests. Also, don’t forget to add yourself to the list of contributors below in your pull requests.
At present, this estimator is maintained by Martin Albrecht. Contributors are:
- Martin Albrecht
- Florian Göpfert
- Cedric Lefebvre
- Rachel Player
- Markus Schmidt
- Sam Scott
[flake8] max-line-length = 120 max-complexity = 16 ignore = E22,E241
If you run into a bug, please open an issue on bitbucket. Also, please check first if the issue has already been reported.
If you use this estimator in your work, please cite
Martin R. Albrecht, Rachel Player and Sam Scott. On the concrete hardness of Learning with Errors.Journal of Mathematical Cryptology. Volume 9, Issue 3, Pages 169–203, ISSN (Online) 1862-2984,ISSN (Print) 1862-2976 DOI: 10.1515/jmc-2015-0016, October 2015
A pre-print is available as
Cryptology ePrint Archive, Report 2015/046, 2015. https://eprint.iacr.org/2015/046
A high-level overview of that work is given, for instance, in this talk.
Parameters from the Literature
The following estimates for various schemes from the literature illustrate the behaviour of the estimator. These estimates do not necessarily correspond to the claimed security levels of the respective schemes: for several parameter sets below the claimed security level by the designers’ is lower than the complexity estimated by the estimator. This is usually because the designers anticipate potential future improvements to lattice-reduction algorithms and strategies. We recommend to follow the designers’ decision. We intend to extend the estimator to cover these more optimistic (from an attacker’s point of view) estimates in the future … pull requests welcome, as always.
sage: load("estimator.py") sage: n = 1024; q = 12289; stddev = sqrt(16/2); alpha = alphaf(sigmaf(stddev), q) sage: _ = estimate_lwe(n, alpha, q, reduction_cost_model=BKZ.sieve) usvp: rop: ≈2^313.1, red: ≈2^313.1, δ_0: 1.002094, β: 968, d: 2101, m: ≈2^11.7 dec: rop: ≈2^410.0, m: 1308, red: ≈2^410.0, δ_0: 1.001763, β: 1213, d: 2332, babai: ≈2^395.5, babai_op: ≈2^410.6, repeat: ≈2^25.2, ε: ≈2^-23.0 dual: rop: ≈2^355.5, m: ≈2^11.1, red: ≈2^355.5, δ_0: 1.001884, β: 1113, repeat: ≈2^307.0, d: 2263, c: 1
sage: load("estimator.py") sage: n = 752; q = 2^15; stddev = sqrt(1.75); alpha = alphaf(sigmaf(stddev), q) sage: _ = estimate_lwe(n, alpha, q, reduction_cost_model=BKZ.sieve) usvp: rop: ≈2^173.0, red: ≈2^173.0, δ_0: 1.003453, β: 490, d: 1448, m: ≈2^11.1 dec: rop: ≈2^208.3, m: 829, red: ≈2^208.3, δ_0: 1.003064, β: 579, d: 1581, babai: ≈2^194.5, babai_op: ≈2^209.6, repeat: 588, ε: 0.007812 dual: rop: ≈2^196.2, m: 1588, red: ≈2^196.2, δ_0: 1.003104, β: 569, repeat: ≈2^135.0, d: 1588, c: 1
sage: load("estimator.py") sage: n = 804; q = 2^31 - 19; alpha = sqrt(2*pi)*57/q; m = 4972 sage: _ = estimate_lwe(n, alpha, q, m=m, reduction_cost_model=BKZ.sieve) usvp: rop: ≈2^129.3, red: ≈2^129.3, δ_0: 1.004461, β: 339, d: 1954, m: ≈2^11.6 dec: rop: ≈2^144.9, m: 1237, red: ≈2^144.9, δ_0: 1.004148, β: 378, d: 2041, babai: ≈2^130.9, babai_op: ≈2^146.0, repeat: 17, ε: 0.250000 dual: rop: ≈2^139.4, m: 2035, red: ≈2^139.4, δ_0: 1.004180, β: 373, repeat: ≈2^93.0, d: 2035, c: 1
sage: load("estimator.py") sage: n = 2048; q = 2^60 - 2^14 + 1; alpha = 8/q; m = 2*n sage: _ = estimate_lwe(n, alpha, q, secret_distribution=(-1,1), reduction_cost_model=BKZ.sieve, m=m) usvp: rop: ≈2^115.5, red: ≈2^115.5, δ_0: 1.004975, β: 288, d: 4013, m: ≈2^12.6 dec: rop: ≈2^127.1, m: ≈2^11.1, red: ≈2^127.1, δ_0: 1.004663, β: 318, d: 4237, babai: ≈2^114.8, babai_op: ≈2^129.9, repeat: 7, ε: 0.500000 dual: rop: ≈2^118.4, m: ≈2^12.0, red: ≈2^118.4, δ_0: 1.004864, β: 298, repeat: ≈2^58.8, d: 4090, c: 3.908, k: 30, postprocess: 13