Apple's epsilon reset problem is real, but it's worth pointing out that they use additional heuristics based on hashing that plausibly add another layer of priv

Apple's epsilon reset problem is real, but it's worth pointing out that they use additional heuristics based on hashing that plausibly add another layer of privacy [1]. Plausibly, not provably, but it's a bit more than just resetting epsilon. I believe Google and Microsoft use similar tweaked forms of differential privacy. In particular, note that all of these companies -- again, going off public papers -- use the "local" variant of differential privacy, which requires less trust on the user's part. The question of "lifetime" differential privacy, for a single user across different computations and datasets, is still fairly open as far as I know. [1] https://machinelearning.apple.com/docs/learning-with-privacy-at-scale/appledifferentialprivacysystem.pdf https://machinelearning.apple.com/docs/learning-with-privacy...