{"slug": "measuring-human-cryptanalysis-effort", "title": "Measuring Human Cryptanalysis Effort", "summary": "Matthew Green launched a new site, bitsofwork.org, that estimates the number of cryptanalyst-hours spent analyzing different public-key algorithms by classifying roughly sixteen thousand candidate papers into cryptanalytic work on ECDLP, structured and plain lattices, and syndrome decoding. The study, conducted by AI models Claude Fable 5.1 and Claude Opus 5.5 with contributions from GPT-6 Astra, converts papers into estimated research-allocation years via a person-level author map and finds lattices have overtaken elliptic curves in this cumulative measure only recently. Green set the questions, made the key methodological decisions and oversaw the work, and the abstract notes the publication-based estimates may substantially overstate actual time spent.", "body_md": "Matthew Green has a [new site](https://bitsofwork.org/) that tries to estimate the number of cryptanalyst-hours that have been spent analyzing different public-key algorithms:\n\n**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...", "url": "https://wpnews.pro/news/measuring-human-cryptanalysis-effort", "canonical_source": "https://www.schneier.com/blog/archives/2026/10/measuring-human-cryptanalysis-effort.html", "published_at": "2026-10-15 16:24:29+00:00", "updated_at": "2026-10-15 16:52:46.438588+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-research", "large-language-models"], "entities": ["Matthew Green", "bitsofwork.org", "Claude Fable 5.1", "Claude Opus 5.5", "GPT-6 Astra", "ECDLP"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/measuring-human-cryptanalysis-effort", "markdown": "https://wpnews.pro/news/measuring-human-cryptanalysis-effort.md", "text": "https://wpnews.pro/news/measuring-human-cryptanalysis-effort.txt", "jsonld": "https://wpnews.pro/news/measuring-human-cryptanalysis-effort.jsonld"}}