I wanted to endorse this video as well. Very interesting. One application in the VC space (perhaps there are others, but this one occurs to me) might be to monitor which fields are selectively disclosed to a specific verifier (in one or multiple proving interactions), and apply the mathematics to know how close the holder is getting to being strongly identified. I was familiar with this idea already, but unaware that there were formally proven mathematics to measure it. Cool stuff! On Mon, Jun 29, 2020 at 6:37 PM Kim Hamilton <kimdhamilton@gmail.com> wrote: > Thanks for sharing this Liam. I'm very interested in mathematical/formal > approaches to privacy guarantees. Any such content will be eagerly absorbed! > > Thanks again! > Kim > > On Fri, Jun 12, 2020 at 12:54 PM Liam McCarty <liam@unumid.org> wrote: > >> A great video on differential privacy in U.S. Census statistics (made by >> MinutePhysics): https://www.youtube.com/watch?v=pT19VwBAqKA. 2020 is the >> first year the Census can guarantee mathematically robust privacy! >> >> This is from way back in September, but thought this would be of interest >> to the community. >> >> Liam >> >> *Liam McCarty* >> Co-Founder of Unum ID <http://www.UnumID.org> >> >>
Received on Tuesday, 30 June 2020 14:58:04 UTC