In a groundbreaking study, Matthew Green has launched a new platform that aims to estimate the human effort allocated to cryptanalysis of various public-key algorithms. By analyzing a pool of approximately 16,000 academic papers, the research categorizes cryptanalytic work on several key assumptions, including Elliptic Curve Discrete Logarithm Problem (ECDLP) and lattice-based cryptography. The study employs advanced AI models, Claude Fable 5.1 and Claude Opus 5.5, for data analysis and presentation, with oversight from Green. Notably, the findings indicate a recent shift where lattice-based cryptographic systems have surpassed elliptic curves in terms of cumulative research focus, although the estimates could be inflated due to the method of data collection used.
For businesses, the implications of this research are significant, especially as the landscape of cryptographic security evolves. Organizations must remain vigilant about the cryptographic algorithms they implement and consider the growing interest in lattice-based systems that are perceived to offer stronger security against quantum attacks. Understanding the distribution of research efforts can help businesses make informed decisions about which cryptographic frameworks to adopt, ensuring they are not only compliant but also resilient against emerging threats. Ultimately, this study highlights the critical role of data and AI in driving forward the field of cybersecurity, emphasizing the need for continual adaptation in strategies to safeguard sensitive information.
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*Originally reported by [Schneier on Security](https://www.schneier.com/blog/archives/2026/10/measuring-human-cryptanalysis-effort.html)*