Brain Computer Interface (BCI) Applications: Privacy Threats and Countermeasures
Authors:
- Hassan TakabiHTHassan Takabi
- Anuj BhalotiyaABAnuj Bhalotiya
- Manar AlohalyMAManar Alohaly
- 1 November 2016
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 39, 102-111
Abstract
In recent years, Brain-Computer Interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them to make inferences about users' thoughts and mental processes. Since there are no specific standards that govern the development of BCI applications, its users are at the risk of privacy breaches. In this work, we perform first comprehensive analysis of BCI App stores including software development kits (SDKs), application programming interfaces (APIs), and BCI applications w.r.t privacy issues. The goal is to understand the way brainwave signals are handled by BCI applications and what threats to the privacy of users exist. Our findings show that most applications have unrestricted access to users' brainwave signals and can easily extract private information about their users without them even noticing. We discuss potential privacy threats posed by current practices used in BCI App stores and then describe some countermeasures that could be used to mitigate the privacy threats.Keywords
This publication has 11 references indexed in Scilit:
- Dynamic time warping: A single dry electrode EEG study in a self-paced learning task2015
- App stores for the brain: Privacy & security in Brain-Computer Interfaces2014
- Privacy for Personal NeuroinformaticsSSRN Electronic Journal, 2014
- Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous DataIEEE Signal Processing Magazine, 2013
- Functional Encryption: Definitions and Challenges2011
- A new approach for EEG feature extraction in P300-based lie detectionComputer Methods and Programs in Biomedicine, 2009
- Neural Markers of Religious ConvictionPsychological Science, 2009
- Differential Privacy: A Survey of Results2008
- Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
- P300-based detection of concealed autobiographical versus incidentally acquired information in target and non-target paradigmsInternational Journal of Psychophysiology, 2006