[kidnoodle]

I’m still slowly working on a location intelligence data union (https://wherelabs.info) - the idea is people install an app and agree in a very upfront way to have their location tracked over time.

The union monetises this by selling privacy preserving aggregates (think ‘where is everyone in London right now’, or ‘where did people commute from?’), and acts on behalf of union members to stop data brokers selling their location data.

[motohagiography]

this is a really fascinating idea. I'm reading it as, instead of seeing my individual identity in a location dataset, it will see -DataUnionMember- with no unique identifier? When two members are in the same place at the same time, you don't know which one went in which direction?

The question of who can de-identify or unmask the data is there, but I could see the capability being required for gov, military, and police, and then as a premium service to customers.

[kidnoodle]

I think at least initially the ‘product’ is datasets which don’t show individuals. You could of course build out a future direction which is differentially private.

More or less my initial approach to this is you take a grid, and you show movements/density on that grid. If necessary you coarsen the grid to avoid reidentification of individuals, and ultimately to get a good picture of the population given the biased sample which is the union membership, you need a statistical model on top which also helps from a privacy perspective.

State actors demanding individual location history is definitely an issue. I have a few possible approaches in mind to defend against that.