Measuring Human Cryptanalysis Effort
Matthew Green’s site estimates cumulative cryptanalyst-years spent on major public-key assumptions from published literature.
Matthew Green published an estimate of how much public cryptanalysis major public-key assumptions have received, drawn from roughly sixteen thousand candidate papers. The analysis covers ECDLP, structured and plain lattices, and syndrome decoding, then converts publications into estimated research-allocation years using an author map. Claude Fable 5.1 and Claude Opus 5.5 built and ran the pipeline, with contributions from GPT-6 Astra, while Green set the questions and methodology. Counts rely only on published work, may overstate actual time, and exclude secret cryptanalysis; lattices have only recently overtaken elliptic curves on this measure.
- About 16,000 candidate papers were screened for cryptanalytic work.
- Lattices only recently surpassed elliptic curves in estimated cumulative effort.
- Unpublished academic work and secret intelligence cryptanalysis are excluded.
- Claude and GPT models built the pipeline under Matthew Green’s oversight.
Full article227 words · extracted from schneier.com · click to collapse
Matthew Green has a new site that tries to estimate the number of cryptanalyst-hours that have been spent analyzing different public-key algorithms:
Abstract: We estimate how much public cryptanalysis each public-key assumption has received, using the literature itself as the instrument. From roughly sixteen thousand candidate papers we classify cryptanalytic work on ECDLP, structured and plain lattices, syndrome decoding and their comparators, then convert papers into estimated research-allocation years through a person-level author map. These publication-based estimates may substantially overstate actual time spent. Lattices have overtaken elliptic curves in this cumulative measure only recently; we express that history as inference budgets under stated productivity assumptions. The study was conducted by AI models: Claude Fable 5.1 and Claude Opus 5.5 built the pipeline, ran the analysis and wrote this page, with contributions from GPT-6 Astra. Matthew Green set the questions, made the key methodological decisions and oversaw the work.
There are a lot of caveats to this analysis. It relies on published work, so anything unpublished does not count. This includes both academic cryptanalysis that didn’t result in a paper, and intelligence agency cryptanalysis done in secret. But it’s a good first stab at a metric of this sort.
Tags: academic papers, algorithms, cost-benefit analysis, cryptanalysis
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Text extracted automatically; images, tables and formatting may be missing. Original: https://www.schneier.com/blog/archives/2026/10/measuring-human-cryptanalysis-effort.html