Bike-Share Data Release Raises Privacy Concerns

London's bike-share program let hackers get close to profiling individual riders
James Siddle, The Variable Tree

Blue bikes. Red faces. Usage data from London’s bike-share program posted to the Internet has been taken down following criticism that it could have allowed individual riders to be identified.

Blogger James Siddle revealed that a publicly available Transport for London dataset covering six months of bike journeys made just over a year ago included unique rider IDs. Siddle says this means that someone who has access to the data can extract and analyse the journeys made by individual cyclists within London during that time. “With a little effort, it's possible to find the actual people who have made the journeys,” he says. 

Siddle constructed maps, an example of which is above, based on the journeys of a handful of individual riders to highlight the privacy issues the data release raises. He was able to make informed guesses about where they lived and worked, and using timestamps, where they likely spent their evenings and nights.

He says he didn’t take the next step to find a piece of data that would connect the dates, times and locations of the journeys to a name. His purpose was to highlight how easily supposedly private aspects of our lives can be exposed. It might only take a trawl of Facebook, Twitter or Flickr postings, he says, to make that connection by overlaying the two sets of location data.

Transport for London, which is the authority that oversees all transportation in the British capital, says the individual ID data was removed as soon as the matter was brought to its attention.

Many cities' transportation authorities make datasets public in the hope hackers will find innovate ways to make use of it. New York City’s Mass Transit Authority runs an annual competition to develop new transportation apps.

Such data releases in the aggregate are harmless enough. Location data linked to individuals, however widely dispersed its component data sets are, can increasingly be mined to build detailed profiles, especially with the ever improving location tracking capabilites of mobile devices. You are where you go.

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