Side channel analysis is the process of examining information leaked by a computing device during use, and leveraging such data to make inferences about various aspects of the system. Historically, side channels have been exploited for malicious purposes, from inferring sensitive data to infringing on the privacy of users. For example, power consumption has been exploited to reveal secret cryptographic keys, and features of wireless network traffic have been leveraged to reveal web browsing activity of a user. The goal of this dissertation is not only to explore the potential of using side channels to determine what types of activity a computing system is engaged in but also study the relationship between the operations performed by the system and the side channel.
In this dissertation we present two key concepts: the application of side channel analysis for security and privacy purposes, particularly for monitoring systems, and the development of a model for defining the relationship between side channel information and the operations performed by the system. The empirical studies presented in this dissertation demonstrate that side channel information can be leveraged to monitor the behavior of systems and describe advantages for doing so over alternative methods. In addition, we outline a model that describes how the operations performed by a system are represented in side channel information and how the information loss can be estimated. The goal of these two directions is to expand the understanding of side channels, their benefits and drawbacks, from both a practical point of view as well as theoretical. Our work shows how the outlined model can measure the information loss in side channels while our empirical studies show that despite information being lost, in many cases, side channels contain enough information to successfully monitor the behavior of systems and provide a non-intrusive, minimal impact method for doing so.