The Odyssey: Modeling Privacy Threats in a Brave New World

Abstract

In the upcoming General Data Protection Regulation (GDPR), privacy by design and privacy impact assessments are given an even more prominent role than before. It is now required that companies build privacy into the core of their technical products. Recently, researchers and industry players have proposed employing threat modeling methods, traditionally used in security engineering, as a way to bridge these two GDPR requirements in the process of engineering systems. Threat modeling, however, typically assumes a waterfall process and monolithic design, assumptions that are disrupted with the popularization of Agile methodologies and Service Oriented Architectures. Moreover, agile service environments make it easier to address some privacy problems, while complicating others. To date, the challenges of applying threat modeling for privacy in agile service environments remain understudied. This paper sets out to expose and analyze this gap. Specifically, we analyze what challenges and opportunities the shifts in software engineering practice introduce into traditional Threat Modeling activities; how they relate to the different Privacy Goals; and what Agile principles and Service properties have an impact on them. Our results show that both agile and services make the end-toend analysis of applications more difficult. At the same time, the former allows for more efficient communications and iterative progress, while the latter enables the parallelization of tasks and the documentation of some architecture decisions. Additionally, we open a new research avenue pointing to Amazon Macie as an example of Machine Learning applications that aim to provide a solution to the scalability and usability of Privacy Threat Modeling processes.