Brain Privacy

Brain-computer interfaces (BCIs) enable non-verbal communication between users and devices by using measured brain signals. In recent years, BCIs have begun to gain popularity in several domains such as medical, gaming, entertainment, personal health, marketing, and cybersecurity. As the brain gives rise to consciousness, our innermost thoughts and our most basic human needs, the issue of privacy of one’s own thoughts and mental processes is of significant importance. The neural devices provide access to users’ unique brainwave signals, which consequently allows entities to make inferences. Government or private organizations might collect and analyze brain signals to extract private information about users’ thoughts, memories, likes, dislikes, beliefs, fears, prejudices, mental state, potential neurophysiological disorders, health, etc. In this project, we study effective solutions to protect the privacy of an individual’s thoughts powered by theoretical foundations and informed by the architecture of neural technologies, BCI applications, and the computations they perform on the neural data.

Related Publications:

  • Hassan Takabi, Anuj Bhalotiya, and Manar Alohaly. (2017). Brain computer interface (BCI) applications: Privacy threats and countermeasures. In Proceedings - 2016 IEEE 2nd International Conference on Collaboration and Internet Computing, IEEE CIC 2016 (pp. 102–111). http://doi.org/10.1109/CIC.2016.24 Pdf  Bibtex
  • Bhalotiya, Anuj Arun. Brain Computer Interface (BCI) Applications: Privacy Threats and Countermeasures, thesis, May 2017; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc984122/: accessed May 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .  Pdf  Bibtex
  • Hassan Takabi, Samir Koppikar, and Saman T. Zargar (2016). Differentially private distributed data analysis. In Proceedings - 2016 IEEE 2nd International Conference on Collaboration and Internet Computing, IEEE CIC 2016. http://doi.org/10.1109/CIC.2016.36 Pdf  Bibtex
  • Koppikar, Samir Dilip. Privacy Preserving EEG-based Authentication Using Perceptual Hashing, thesis, December 2016; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc955127/: accessed May 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; . Pdf  Bibtex