Matthew Stephenson

This research note describes the novel application of Zero-Knowledge Proofs to conduct data analysis. A zero-knowledge proof is useful to prove you can generate a particular result without revealing certain parts of the process. Using such a proof, in the setting of a crowdsourced dataset testing Seth Roberts’ Appetite Theory, we were able to conduct an independent data analysis of unshared data. The analysis queried the data set to generate both a numerical result and a computational proof which illuminated the dataset without revealing it. We suggest that this protocol could be useful for solving the “other” file drawer problem, where researchers naturally seek to horde and protect their private data until they have extracted maximal value from it. And we further suggest and outline an application in citizen science.

Glyphs are a proposed system that comprises non-fungible digital tokens for first manuscripts. Author-produced first manuscripts have been uniquely valuable since the dawn of the printing press \citep{plant1934copyright} and digitally scarce manuscript NFTs can similarly be used to reward thinkers, innovators, and creators. We believe that increased rewards for the time-consuming creation and early discovery of original work is essential in the larger project of improving online knowledge creation and dissemination. Of particular interest is a seemingly global decline in innovation which may be partially alleviated by behavioral and market mechanisms which can reward citizen science, ``tinkering'', and perhaps even basic research.