• DOI:10.1145/2897845.2897847
• Corpus ID: 207236218

Friend or Foe?: Your Wearable Devices Reveal Your Personal PIN

@article{Wang2016FriendOF,
  title={Friend or Foe?: Your Wearable Devices Reveal Your Personal PIN},
  author={Chen Wang and Xiaonan Guo and Yan Wang and Yingying Chen and Bo Liu},
  journal={Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security},
  year={2016}
}

• Chen Wang, Xiaonan Guo, +2 authors Bo Liu
• Published 30 May 2016
• Computer Science
• Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security

The proliferation of wearable devices, e.g., smartwatches and activity trackers, with embedded sensors has already shown its great potential on monitoring and inferring human daily activities. This paper reveals a serious security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people accessing key-based security systems. Existing methods of obtaining such secret information relies on installations of dedicated hardware (e.g., video camera or fake keypad), or training with labeled data from body sensors, which restrict use cases in practical adversary scenarios. In this work, we show that a wearable device can be exploited to discriminate mm-level distances and directions of the user's fine-grained hand movements, which enable attackers to reproduce the trajectories of the user's hand and further to recover the secret key entries. In particular, our system confirms the possibility of using embedded sensors in wearable devices, i.e., accelerometers, gyroscopes, and magnetometers, to derive the moving distance of the user's hand between consecutive key entries regardless of the pose of the hand. Our Backward PIN-Sequence Inference algorithm exploits the inherent physical constraints between key entries to infer the complete user key entry sequence. Extensive experiments are conducted with over 5000 key entry traces collected from 20 adults for key-based security systems (i.e. ATM keypads and regular keyboards) through testing on different kinds of wearables. Results demonstrate that such a technique can achieve 80% accuracy with only one try and more than 90% accuracy with three tries, which to our knowledge, is the first technique that reveals personal PINs leveraging wearable devices without the need for labeled training data and contextual information.