There are downsides with downloading their app just to input bad data, but it's a fun thought.
edit: While we're at it we might as well offer an alternative app to people.
I posted in !opensource@programming.dev to collect recommendations for better apps
The post: https://lemmy.ca/post/32877620
Leading Recommendation from the comments
The leading recommendation seems to be Drip (bloodyhealth.gitlab.io)
Summarizing what people shared:
- accessible: it is on F-droid, Google Play, & iOS App Store
- does not allow any third-party tracking
- the project got support from "PrototypeFund & Germany's Federal Ministry of Education and Research, the Superrr Lab and Mozilla"
- Listed features:
- "Your data, your choice: Everything you enter stays on your device"
- "Not another cute, pink app: drip is designed with gender inclusivity in mind."
- "Your body is not a black box: drip is transparent in its calculations and encourages you to think for yourself."
- "Track what you like: Just your period, or detect your fertility using the symptothermal method."
Their Mastodon: https://mastodon.social/@dripapp
I think that's overestimating the complexity. In my example you can just delete all data from people who cross the border regularly. I heard like >80% of Americans don't travel that much. So you'd still catch the vast majority. And there are additional giveaways. Visiting relatives will follow a pattern or coincide with holidays like every other thanksgiving. Weekend trips will start at the end of a week while work will be during the week and often someone would visit a worksite multiple times.
And correlating data and having multiple datapoints helps immensely. For example if you want to correlate license plates with cell tower data: One measurement will only narrow it down to a few hundreds or thousands of people who passed the highway at that point. But, a single additional datapoint will immediately give an exact answer. Because it's very unlikely that multiple of the people also return at the same time. Same applies to other statistics.
And you don't even need to figure out the patterns. It's a classification problem. And that's a well understood problem in machine learning. You need a labeled dataset with examples and ML will figure out the rest. No matter if it's deciphering hand writing, figuring out shopping behaviour to advertise, or something like this. We figured out the maths a long time ago. Nowadays it's in the textbooks and online courses and you just need some pre-existing data to start with. Maybe you're right and compiling a dataset will take more than 3 weeks. But it's certainly doable and not that complicated. And menstrual cycles follow patterns. That makes machine learning a precise approach. It'll home in on the ~4weeks cycle, find outliers and data that never followed a realistic cycle.
I agree, there are complications. People need to be incentivised to pay attention. Government agencies regularly fail at complex tasks. Due to various reasons. But it's probably enough to make peoples' lives miserable if they have to live in constant fear. So there is an additional psychological factor, even if they don't succed with total surveillance.
And this approach is a bit unlikely anyways. It's far easier to pass a law to force clinics to rat out people or something like that.
But my guess is that [predictive policing](https://en.wikipedia.org/wiki/Predictive_policing might become an issue. Currently we seem to stick to intelligence agencies and advertising with that technology (and Black mirror episodes and China). But that's mainly a political choice.