Researchers Re-identify 99.98% People In ‘Anonymized’ Dataset

Researchers De-Anonymize Dataset people
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Various companies collect data from our devices almost all the time. While there is always a privacy concern in the picture, they try to assure that our data is in completely safe hands. Also, if it gets shared with third-parties, all the information that could be used to identify people is redacted and de-identified.

Turns out the techniques used to anonymize data aren’t that fool-proof, according to researchers at Imperial College London who have published a paper on reverse engineering incomplete datasets.

The researchers developed a machine learning model that can reverse-engineer an incomplete dataset. Using 15 demographic attributes such as age, gender, marital status, etc. they were able to re-identify almost 99.98% Americans in an anonymized dataset.

For that purpose, the researchers used 210 different datasets covering a “large range of uniqueness.” It includes information on around 11 million Americans.

However, the goal of the study isn’t to establish the fact that the so-called “anonymous” datasets can be deanonymized. It was already done in the past at DEFCON 2018, where hackers were able to legally get hold of the browsing history of 3 million Germans, and de-anonymize them.

Researchers have made an attempt to prove how easy it has become to fool the techniques used to make the datasets. It invites a call to action for governments and companies to implement even robust techniques that can keep people’s identities secure.

They have also set up a website where you can check how easy it is to identify you in an anonymous dataset.

Also Read: VLC Media Player Has Critical Security Flaw: Uninstall Now!
Aditya Tiwari

Aditya Tiwari

Aditya likes to cover topics related to Microsoft, Windows 10, Apple Watch, and interesting gadgets. But when he is not working, you can find him binge-watching random videos on YouTube (after he has wasted an hour on Netflix trying to find a good show). Reach out at [email protected]
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