My Mouse, My Rules: Privacy Issues of Behavioral User Profiling via Mouse Tracking
It addresses privacy risks from behavioral profiling on the web, though it is incremental as it builds on existing tracking and adversarial techniques.
The paper demonstrates that mouse cursor movements can be captured at scale to predict user demographics with reasonable accuracy using minimal code, and proposes an adversarial method with a browser extension to mitigate such profiling.
This paper aims to stir debate about a disconcerting privacy issue on web browsing that could easily emerge because of unethical practices and uncontrolled use of technology. We demonstrate how straightforward is to capture behavioral data about the users at scale, by unobtrusively tracking their mouse cursor movements, and predict user's demographics information with reasonable accuracy using five lines of code. Based on our results, we propose an adversarial method to mitigate user profiling techniques that make use of mouse cursor tracking, such as the recurrent neural net we analyze in this paper. We also release our data and a web browser extension that implements our adversarial method, so that others can benefit from this work in practice.