The slides from my presentation at the Homomorphic Encryption Standardization Consortium meeting on 6 February 2020 at Microsoft Research in Seattle (archived): https://docs.google.com/presentation/d/1VakPf8i205la3lIdY7Grt1fTIg3qY9AhOobpt9EuZZs/

The slides from my briefing on the ITU-WHO Focus Group on Artificial Intelligence for Health at the Roundtable on International Actors in Digital Health (archived): https://docs.google.com/presentation/d/1_U0ddCvdDLcbZc6D1OV1qxZYFfDCh0hTfSiDOp0kVsM/ Some photos were taken (CC-BY-$.0): 

Earlier this year I was in China, and in order to communicate with local delegates I installed WeChat. WeChat has a very handy feature that replaces exchanging telephone numbers manually, and then waiting until WhatsApp picks up the new contract from the contracts app, by a QR code. One person brings up his QR code from within the app and the other person scans this code, and that’s it! you are connected.

The slides from my briefing on the ITU-WHO Focus Group on Artificial Intelligence for Health to Study Group 2 of the ITU Development Sector at their Workshop on AI and Emerging Technologies (archived): https://docs.google.com/presentation/d/1wSYUeP8COsCw9iQSvJ25_HcIheykEwc6g_NfrGZnzAY/ The photographer took some photos, here’s one (CC-BY-4.0): 

Homomorphic encryption is allows computations to be performed on encrypted data. This has enormous potential in areas of machine learning that deal with private data, such as medical records. Below is an implementation of homomorphic encryption in R. It encrypts two pieces of data m=10 and m1=2, once they are encrypted (as cipher and cipher2 respectively), the two encrypted forms can be added up together (cyphertotal). They can then be descrypted to reveal mess2 to equal 12 (i.e. the sum of 10 and 2).