3 ms·
Airnb and the cell provides are good examples [1],[2]. The cell provider location data is the most insidious. They add noise to it, but the central limit theo
by rmrfstar 6y ago
Airnb and the cell provides are good examples [1],[2].
The cell provider location data is the most insidious. They add noise to it, but the central limit theorem is a real thing and people who buy the data are aware of that.
[1] https://www.nytimes.com/2020/01/15/technology/data-privacy-law-access.html https://www.nytimes.com/2020/01/15/technology/data-privacy-l...
[2] https://www.vice.com/en_us/article/nepxbz/i-gave-a-bounty-hunter-300-dollars-located-phone-microbilt-zumigo-tmobile https://www.vice.com/en_us/article/nepxbz/i-gave-a-bounty-hu...
- derivativethrow 6y agoI’m aware of what the central limit theorem is, but I’m confused as to what you mean here. Do you mind clarifying? It sounds like you’re saying the CLT leads to deanonymization. I don’t see how the two are related.
- rmrfstar 6y agoTake several measurements and average them. The variance scales like 1/n_samples, which can take you from city-block resolution to building resolution quite rapidly.
- unishark 6y agoThere are conditions for the central limit theorem to hold though. One can generate random noise which violates them. It's certainly hard to guarantee privacy, but it's not trivially easy to hack if they're halfway smart about it.