Tracking Private Browsing Sessions using CPU-based Covert Channels
Nikolay Matyunin, Nikolaos Athanasios Anagnostopoulos, Spyros Boukoros, Markus Heinrich, André Schaller, Maksim Kolinichenko, Stefan Katzenbeisser · 2018
In this paper we examine the use of covert channels based on CPU load in order to achieve persistent user identification through browser sessions. In particular, we demonstrate that an HTML5 video, a GIF image, or CSS animations on a webpage can be used to force the CPU to produce a sequence of distinct load levels, even without JavaScript or any client-side code.