Privacy analysis of mobile analytics
Many companies require the gathering of user behavioural data from mobile devices and device applications in order to: i) improve their products, ii) monitor their systems, iii) engage in targeted marketing, etc. Significant privacy concerns exist with the measurement and storage of such data. In general, existing research highlights that data anonymization can be insufficient to fully address these risks, i.e. that anonymized data can be clustered and correlated with external sources to reveal private information. The emerging legal and statutory frameworks around privacy entail that there is beginning to be a strong need to be able to quantify privacy issues in terms of their resultant business risks, as well as a need to be able to properly restructure business solutions to mitigate these risks. The focus of this internship is to develop formal quantitative methodologies to explore these issues within the limited context of a specific set of industry-held data sets within the domain of assessing user quality of experience (QoE) for mobile device apps. The lessons learned via analyzing these issues with respect to this data will then be provide the basis for a case study which will then, ideally, lead to a more generalized solution approach to these issues. This work will be conducted as part of the University of Victoria’s Entrepreneurial Engineering Masters Program, and hence, under the strong guidance of Wesley Clover and the Alacrity Foundation such that the full intension is to produce industry-usable and applicable results.