In the paper "Friend or Foe?: Your Wearable Devices Reveal Your Personal PIN" scientists from Binghamton University and the Stevens Institute of Technology combined data from embedded sensors in wearable technologies, such as smartwatches and fitness trackers, along with a computeralgorithm to crack private PINs and passwords with 80-percent accuracy on the first try and more than 90-percent accuracy after three tries.Yan Wang, assistant professor of computer science within the Thomas J. Watson School of Engineering and Applied Science at Binghamton University is a co-author of the study along with Chen Wang, Xiaonan Guo, Bo Liu and lead researcher Yingying Chen from the Stevens Institute of Technology. The group is collaborating on this and other mobile device-related security and privacy projects."Wearable devices can be exploited," said Wang. "Attackers can reproduce the trajectories of the user's hand then recover secret key entries to ATM cash machines, electronic door locks and keypad-controlled enterprise servers."Researchers conducted 5,000 key-entry tests on three key-based security systems, including an ATM, with 20 adults wearing a variety of technologies over 11 months. The team was able to record millimeter-level information of fine-grained hand movements from accelerometers, gyroscopes and magnetometers inside the wearable technologies regardless of a hand's pose. Those measurements lead to distance and direction estimations between consecutive keystrokes, which the team's "Backward PIN-sequence Inference Algorithm" used to break codes with alarming accuracy without context clues about the keypad.
According to the research team, this is the first technique that reveals personal PINs by exploiting information from wearable devices without the need for contextual information.
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