Stanford and Princeton researchers have been able to identify 70% of Internet users by comparing their web-browsing history to publicly-available information on social networks, according to a report by The Stack.
Their study was titled De-anonymizing Web Browsing Data with Social Networks, and found that the “identities to 374 sets of apparently anonymous browsing histories” could be discovered by following connections between “links shared on Twitter feeds” and “the likelihood a user would favour personal recommendations over abstract web browsing”.
Users in the test group were given a Chrome extension which extracted their browsing history – after which the researchers used Twitter’s URL-shortening protocol to identify t.co links.
“81% of the top 15 results of each enquiry run through the de-anonymisation program contained the correct re-identified user – and 72% of the results identified the user in first place,” stated the report.
While the browsing history is not directly accessible to websites, data brokers and advertisers can “gather up sufficient history via tracking cookies” to conduct identification.
“The trail only leads as far as a Twitter user ID, and if a user is pseudonymous, further action would need to be taken to affirm their real identity.”
The report stated that the use of HTTPS connections does not mask the URL, while VPNs do not prevent tracking cookies.