bitcoin and cryptography for statistical inference and AI

A recent news editorial in Nature (15 March issue) reminded me of the lectures Louis Aslett gave at the Gregynog Statistical Conference last week, on the advanced use of cryptography tools to analyse sensitive and private data. Lectures that reminded me of a graduate course I took on cryptography and coding, in Paris 6, and which led me to visit a lab at the Université de Limoges during my conscripted year in the French Navy. With no research outcome. Now, the notion of using encrypted data towards statistical analysis is fascinating in that it may allow for efficient inference and personal data protection at the same time. As opposed to earlier solutions of anonymisation that introduced noise and data degradation, not always providing sufficient protection of privacy. Encryption that is also the notion at the basis of the Nature editorial. An issue completely missing from the paper, while stressed by Louis, is that this encryption (like Bitcoin) is costly, in order to deter hacking, and hence energy inefficient. Or limiting the amount of data that can be used in such studies, which would turn the idea into a stillborn notion.