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If someone can use that information to identify you, then the data is by definition not anonymized. It doesn't matter how exceptional the circumstances where that's allowed to happen are. "Fully 100% anonymous, unless we label you a terrorist" is not the same as anonymous.

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If you're going to nitpick like that, then no data is ever anonymized if it contains any information at all. When you start combining pieces of data, they all contribute information that helps you narrow down individuals until there is only one possible match.

It's not nitpicking. Your definition of "anonymized" leads people to believe they are anonymous when they are not. That can lead to serious consequences.

It’s not my definition. You’re twisting words. Location data is just not “anonymizable” at all because it’s always possible to combine it with other sources.

Read up on differential privacy and k-anonymization. There are commonly implemented best practices for measuring and preserving anonymity in a dataset in non-reversible ways. It usually involves aggregating clusters of data and dropping clusters with too few unique contributions.

These techniques have a long track record in the private sector and with public entities such as the US Census, with a lot of formal research to back it up.

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