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
We'd need to identify some threat model to continue the discussion. I don't know what people are afraid of. I'd say the other way round is more likely. For example a state decides to pursue people terminating a pregnancy. They can use data from telecommunications providers to find out which phones cross the border to the neighboring state and return the same or the next day. Disregard people who do it regularly, and then correlate that data to other factors. Like pull up the menstrual tracker account that was accessed by that specific IP address.
We know since Snowden that some agencies do similar things (supposedly for terrorism) and generally a lot of logs are kept. Also we have lots of automatic license plate readers and additional surveillance available.
Aside from that, it is spread that Amazon knows if you're pregnant before you do. They could also buy the data who is interested in romper suits, supplements or other specific things and then isn't. I suppose it's not exactly about that... More that Amazon have some good heuristics and algorithms to predict things from general shopping behaviour. And you could also do the same thing to menstrual tracking. The cycle is pretty regular. And then it usually stops once someone gets pregnant. And I believe after that it takes some time to settle down to a very regular pattern again. You could easily detect that with an algorithm. And simultaneously get rid of artificial (spammed) data that doesn't follow what is possible. Probably takes a skilled programmer like 3 weeks and then you can tell if an account owner is real, and probably even if they take some contraceptive or not, due to the slight variations. And if an app has some recommendations features, they're likely to already include the groundworks for data analyzing.
Ultimately, the government already analyzes and stores the data from telco providers. And it's always easier to combine several factors to make good predictions, than to rely on a single source. And I'd say this kind of surveillance has to be done automatically, anyways. It's almost never feasible to sift through databases manually.
Ok, let's use your first example. Someone crosses into a neighboring state and returns in the same day...I had co-workers who did that every day.
Let's narrow that down... You cross into another state with abortion care once and return in the same day. Or maybe you're a salesman closing a deal. Or maybe you're visiting family and have work tomorrow... And honestly, both those situations are far more frequent. That happens every day. It happens more if you live near the border - otherwise you probably got a hotel. Unless you can't afford a hotel. And the list goes on - all this structured data turns into stories at some point
Here's the thing. Prism could handle it, because it's a ton of people on the payroll
The government is not a monolith though...9/11 is a great example. We knew it would happen, we knew it was planned, but the right people didn't know in the right time, because the agencies are not a monolith.
Because that is the hard part - communication is hard, harder with security concerns. More data means more analysts reviewing it - you can collect all the data you could want , (and we do), you could hire all the analysts you can afford (and we do), but that still gives you severe limits
We're actually pretty great at stopping terrorism, but we do that (in part) because we have all this data and use it for specific ends
None of this shit is easy - I used to do this, specifically. How do you take 15 data sources that sometimes conflict, and deconflict them? There's no hierarchy of truth here. This is literally a cutting edge problem - it's a literal holy Grail. No one can solve it in 3 weeks, or even 3 years
You want a 20% rate? I could give it to you tomorrow, poisoned data or no, I could give it to you in weeks... Maybe not 3, because that's a shit ton of data sources, but with proper motivation I could pump it out.
You want 90%? Give me a century or two, and I'm good at this. Maybe a genius could give it to you in a lifetime of with
It's like they say in game dev, you can do 90% in 10% of the time, but the last 10% takes 90% of the time. And that's a solved problem.
Except this is an unsolved problem, possibly the most lucrative unsolved problems in history
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.