"Differential privacy" (previously) is a promising, complicated statistical method for analyzing data while preventing reidentification attacks that de-anonymize people in aggregated data-sets.
Journalist's Resource published this great comic by Josh Neufeld, explaining the basic concepts behind differential privacy, the data collection method used to prevent bad actors from de-anonymizing the information gleaned from the 2020 Census.
The original source includes some other great resources on differential privacy, but since the comic itself is made available under a Creative Commons Attribution-NoDerivatives 4.0 International License, we've re-posted it here in full. — Read the rest
Buried in the new Apple Iphone and Apple TV privacy policy is an unannounced program that uses "information about how you use your device, including the approximate number of phone calls or emails you send and receive…to compute a device trust score when you attempt a purchase."
Even the most stringent privacy rules have massive loopholes: they all allow for free distribution of "de-identified" or "anonymized" data that is deemed to be harmless because it has been subjected to some process.
