3 ms·
I don't want my privacy invaded as much as the next person, but arguing about broadness of medical history in this context is pretty stupid. The data needs to
by tomxor 10y ago
I don't want my privacy invaded as much as the next person, but arguing about broadness of medical history in this context is pretty stupid.
The data needs to be broad if you are interested in finding out things you don't already know... that's why it's being fed into a machine learning algorithm in the first place - If you get selective then it's not going to be very useful - how do you limit history to what's relevant when you don't know whats relevant?
There is however an interesting difference here between most data mining on the web which is trying to sell advertising. In this instance it should be fully anonymisable, only the doctor should be allowed to see that patient ID e76f57a is John Smith.
- 3JPLW 10y agoNote that there's a big difference between de-identifying and fully anonymizing data. In general, it's extremely hard to fully anonymize a dataset. De-identification gets you most of the way there, but its frequently possible for a dedicated attacker to re-identify users by combining the data with public datasets. For example, patient ID e76f57b may be a 38 year old woman in Smalltown. Her medical record states that she gave birth on 3-20-2017. Find all public birth announcements from that day in that town with a 38 year old mother. Once you have a small set of candidates, it's not hard to narrow down farther.