Many people assume that if their name and phone number are not attached to a piece of data, it cannot really be about them. On Shawn Ryan Show #340, Byron Tau explains why that assumption does not hold up for phone location data.
Clip: Viral Podcast Shorts YouTube Short from the full source linked below.
The segment used in the Short runs just under a minute. It covers where this kind of data comes from, what "anonymized" means in practice and why it is often not that hard to work out who is behind a device. The details below stay with what he says.
Where the location data comes from
Tau starts with the source. The data, he says, comes off apps like weather apps and games. He adds that it comes when you opt in, which means the person sharing it has, at some point, agreed to share their location with an app.
He does not name specific apps or companies in the clip, and this article does not add any.
What "anonymized" actually means
Next, Tau describes what is and is not in the data. "Your phone number's not in there, your name isn't in there," he says. Instead, the device is known by "an anonymized identifier, a string of letters and numbers."
On paper, that sounds private. A random string is not a name, and it does not look like anyone in particular. But Tau's point is that the identifier is only part of the picture. The other part is where the phone goes, and that movement can say far more than a label.
"We can generally figure out who you are," he says.
Night and day tell the story
The key, as Tau explains it, is the pattern. Where a phone is at night, where it goes during the day and where it returns at night again is, in his words, a "pretty good correlation" to a person's real-world movement and identity.
Put simply, most people sleep in one place and spend their working hours in another. Line those two places up and the list of people who match both can become very short.
Tau does not walk through a step-by-step method in the clip. His argument rests on that simple idea: a home location and a work location, taken together, can be enough to narrow things down.
His own example from Washington, D.C.
Tau makes it personal. He says he is the only person who gets up at his home in Washington, D.C., goes to his office in downtown Washington and comes home every night.
Even someone living at the same address does not share that exact pattern. His fiancée, he notes, wakes up there too, but she goes to a different office. So her phone and his would not trace the same daily route.
That is why, he says, "it's not all that difficult to get down to people's identity" in these data sets. The identifier stays anonymous on paper, but the routine is distinctive.
Why it surprised him
Tau closes the segment by looking back at how this landed when he first heard about it. He says he thought it was "rather extraordinary" and a kind of surveillance he "just hadn't considered."
That reaction may be the most relatable part of the clip. The pieces he describes are ordinary: a weather app, a game, an opt-in prompt, a random string of characters. It is the combination, tracked over days and nights, that turns them into something that can point to one person.
The clip stays at that level. It does not go into who buys this data or how it is used, and this article covers only the segment shown in the Short. The fuller conversation is in the full episode.
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FAQ
Where does phone location data come from, according to Byron Tau?
On Shawn Ryan Show #340, he says it comes off apps like weather apps and games, when users opt in.
Is the data attached to a name or phone number?
He says no. The device is known by an anonymized identifier, a string of letters and numbers.
How can anonymous location data still identify someone?
Tau says where a phone is at night and where it goes during the day correlates closely with a person's real-world movement and identity.
What example does he give?
He says he is the only person who goes from his home in Washington, D.C., to his downtown office and back every day. His fiancée lives there too but goes to a different office.