AI Can Identify People Even in Anonymized Datasets

Advancements in AI might soon render phrases such as “hidden in the crowd” or “stay hidden in plain sight” a curious relic of the past, according to new research published last week on Nature Communications.

In a paper titled “Interaction data are identifiable even across long periods of time,” researchers used geometric deep learning and triplet loss optimization to successfully identify a majority of individuals from an anonymized mobile phone dataset of 40,000 people.

Why it matters

The research is notable because fine-grained records of people’s interactions, both offline and online, are collected at scale today.

Tech giants such as Facebook and Google, telecommunication operators, and other businesses are known to collect and either resell data wholesale or leverage it to power data-centric services.

The technique relies on how people tend to stick to established social circles and that such regular interactions form a stable pattern over time. By leveraging mobile phone interaction data and Bluetooth close-proximity data, the researchers successfully connected the dots between user interactions to identify people.

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