Surveillance is the act of collecting, analysing, and acting upon information about specific objects, data, or individuals. Recent advances have allowed for the automation of a large part of this process. Of particular interest is the use of computer algorithms to analyse surveillance data. We refer to surveillance that uses this form of analysis as *algorithmic surveillance*. The rapid growth of algorithmic surveillance has left many important questions unasked. Counter-surveillance is the task of making surveillance difficult. To do this, it subverts various components of the surveillance process. Much like surveillance, counter-surveillance has many applications. It is used to critically assess and validate surveillance practices. As well, counter-surveillance serves to protect privacy, civil liberties, and against abuses of surveillance. Unfortunately, counter-surveillance techniques are often considered to be of little constructive use. As such, they are underdeveloped. At present, no counter-surveillance techniques exist that are able to adequately address algorithmic surveillance. In order to develop counter-surveillance methods against algorithmic surveillance, the *process* of surveillance must first be understood. Understanding this process ensures that the necessary components of algorithmic surveillance will be identified and subverted. As such, our research begins by developing a model of the surveillance process. This model consists of three distinct stages: the collection of information, the analysis of that information, and a response to what has been discovered (the action). From our analysis of the structure of surveillance we show that counter-surveillance techniques prior to now primarily address the collection and action stages of the surveillance process. We argue that the neglect of the analysis stage creates significant problems when attempting to subvert algorithmic surveillance, which relies heavily upon a complex analysis of data. As such, we go on to demonstrate how algorithmic analysis may be subverted. To do this, we develop techniques that are able to subvert three common algorithmic analysis techniques: classification, cluster analysis, and association rules. Each of these attacks against algorithmic analysis works surprisingly well and demonstrate significant flaws in current approaches to algorithmic surveillance. / Thesis (Master, Computing) -- Queen's University, 2007-09-18 10:42:21.025