```Based on a binomial/Poisson distribution and a baseline of 21 million U.S. device sales per release, a fingerprint relying on "seconds since setup" fails to uniquely identify individuals. In the high-density Early Adopter phase, you will share your exact setup second with an average of 1.01 other people (a total matching pool of ~2 people). Six months into the cycle, you will still share that second with an average of 0.68 other people.```
replyIn the U.S., device setup time (to the second) very conservatively gets you clubbed into a single group of 100 individuals as an "advanced persistent threat" tracker. Even compressing activations to "80/20 during business hours" the math kindof maxes out at a pool of ~5 people, and assuming worst case "20x" of that still means you're still pretty darned identifiable.
If you get ~6-8 more bits of entropy (eg: Device Type + Capacity is easily 2-3 bits, and Time Zone is probably another 2-3 bits) you're cooked!
Reminds me of a meeting I was party to with the Safari team. We worked with them on some standards stuff at an old job. They claimed to have creepy-level tracking of users back then. We were discussing how to identify users for an A/B test across millions of sites and comparing what fingerprints we could both derive to most likely end up on the same user.
replyIf you use a closed source browser. That’s the kinda shit they do.
Are you claiming the Safari team is fingerprinting their users?
replyJust using IP address, device storage, device name, and similar signals, we can identify a user. It isn’t difficult to correlate these data points. Apps like Facebook also force developers to use their SDKs for even small features.
replyYeah, but IP address is "obviously" correlated with a distinct/persistent tranche of users. It's surprising that volume c_time is both more persistent as well as more unique than IP.
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