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Novel Technique Tracks More Web ­sers Across Browsers

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Commuters use their cellphones while waiting for a train.

A team of researchers has developed the first cross-browser fingerprinting technique to use machine-level features to identify users.

Credit: Peter Howell/iStock

Researchers at Lehigh University and Washington University in St. Louis say they have developed the first cross-browser fingerprinting technique using machine-level features to identify users.

The technique takes advantage of many new operating system and hardware features, especially those involving computer graphics, in both single- and cross-browser fingerprinting.

The researchers say their approach accurately fingerprints 99.24% of users, compared with 90.84% for the current state-of-the-art technology, while using the same dataset for single-browser fingerprinting. In addition, they say the new technique can achieve higher uniqueness rates than only the cross-browser approach with similar stability.

The new approach is highly reliable, as the removal of any single feature decreases accuracy by at most 0.3%.

The team collected data via crowdsourcing, asking participants to visit their site with two different browsers and encouraging them to use a third browser.

From Lehigh University
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Abstracts Copyright © 2017 Information Inc., Bethesda, Maryland, USA


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