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:-) Thanks for the detailed response. Let me take a look and get back to you.

My whole point is that you can’t trust that it’s impossible to de-anonymize data simply because some organization removes all of what they believe to be identifying data.

GPS data is a fairly obvious one which is why I brought it up. Just because you remove all identifying info about a GPS trace doesn’t stop someone (or some program) from re-attributing that data based on the start/stop locations of those tracks.

Looking at all the links you’ve posted… so there’s been cases and studies stating that data can re-identified, but do we have insight into what exact data sets they were looking it at? I tried looking at the Nature study but it doesn’t say how they got the data and what exact vectors they were looking at outside of mention of 15 some parameters such as zip code, address etc. Data pipelines and implementation of metrics vary vastly, per implementation, I’m curious to see where the data set came from, what the use case was for collection, the company behind it, the engineering chops it has etc.

If from a data collection standpoint you’re collecting “zip code” and “address”, you’ve already failed to adhere to good privacy practices, which is what I’m arguing in Apple’s case. You could easily salt and hash a str to obfuscate it, why is it not being done? Data handling isn’t any different than a typical technical problem. There’s risks and benefits associated to an implementation, the question is how well you do it and what are you doing to ensure privacy. The devil is in the detail. Collecting “zip code” and “address” isn’t good practice, so no wonder data become re-identifiable.

https://youtu.be/8JxvH80Rrcw https://www.engadget.com/apple-phone-usage-data-not-anonymous-researchers-185334975.html https://gizmodo.com/apple-iphone-privacy-settings-third-lawsuit-1850000531

More FUD. Why aren’t they testing iOS 16? Ok, sure, it’s sending device analytics back… but it could just be a bug? The YT video is showing typical metrics, this isn’t any different to literally any metrics call an embedded device makes. A good comparison would be an Android phone’s metrics call and comparison to it side by side. I’m sorry, I refuse to take seriously a video that says “App Store is watching you” and tries my skews my opinion prior to showing my the data. The data should speak for itself. I see the DSID bit in the Gizmodo article, but that’s a long shot, without any explanation of how to the data is identifiable specifically.

Lastly,

As for your TechRadar link to Apple’s use of E2EE, that’s great, I’m glad they are using E2EE, but that’s not really relevant to our discussion about anonymizing data and risks running afoul of the #3 point you made for why you are frustrated with the majority of users in this post.

Privacy is fundamental to designing a data pipeline that doesn’t collect “zip code” in plain str if you want to data to be anonymized at any level. So it is absolutely relevant. :-)

Edit: To clarify, if it wasn’t clear, relying on just data anonymization and collecting everything under the sun isn’t a good way to design a data pipeline that allows for metrics collection. The goal should always be collecting as little as possible, then using masking, anonymization and other techniques to obfuscate it all. No solution is perfect, but that doesn’t there aren’t shitty ways of implementing things leading to the fiascos you see on the web.