Biblio

Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their designs and evaluate their privacy properties with these same data. In this paper, we aim to understand the impact of this decision on the level of privacy these LPPMs may offer in real life when the users' mobility data may be different from the data used in the design phase. Our results show that, in many cases, training data does not capture users' behavior accurately and, thus, the level of privacy provided by the LPPM is often overestimated. To address this gap between theory and practice, we propose to use blank-slate models for LPPM design. Contrary to the hardwired approach, that assumes known users' behavior, blank-slate models learn the users' behavior from the queries to the service provider. We leverage this blank-slate approach to develop a new family of LPPMs, that we call Profile Estimation-Based LPPMs. Using real data, we empirically show that our proposal outperforms optimal state-of-the-art mechanisms designed on sporadic hardwired models. On non-sporadic location privacy scenarios, our method is only better if the usage of the location privacy service is not continuous. It is our hope that eliminating the need to bootstrap the mechanisms with training data and ensuring that the mechanisms are lightweight and easy to compute help fostering the integration of location privacy protections in deployed systems.

Today, connectivity is demanded in almost every domain, e.g., the smart home domain and its connected smart household devices like TVs and fridges, or the industrial automation domain, connecting plants, controllers and sensors to the internet for purposes like condition monitoring. This trend amplifies the need for secure applications that can protect their sensitive data against manipulation and leaks. However, many applications are still built without considering security in its design phase, often it is perceived as too complicated and time consuming. This is a major oversight, as fixing vulnerabilities after release is often not feasible when major architecture redesigns are necessary. Therefore, the software developer has to make sure that the developed software architecture is secure. Today, there are some tools available to help the software developer in identifying potential security weaknesses of their architecture. However, easy and fast to use tools that support the software developer in improving their architecture's security are lacking. The goal of my thesis is to make security improvements easily applicable by non-security and non-architecture experts by proposing systematic, easy to use and automated techniques that will help the software developer in designing secure software architectures. To achieve this goal, I propose a method that enables the software developer to automatically find flaws and weaknesses, as well as appropriate improvements in their given software architecture during the design phase. For this method, I adopt Model-Based Development techniques by extending and creating Domain-Specific Languages (DSL) for specifying the architecture itself and possible architectural improvements. Using these DSLs, my approach automatically suggests security enhancing improvements for the architecture, promoting increased security of software architectures and as such for the developed applications as a whole.

This paper addresses the potential danger using integrated circuits which contain malicious hardware modifications hidden in the silicon structure. A so called hardware Trojan may be added at several stages of the chip development process. This work concentrates on formal hardware Trojan detection during the design phase and highlights applied verification techniques. Selected methods are discussed and their combination used to increase an introduced “Trojan Assurance Level”.