When Whereabouts is No Longer Thereabouts:Location Privacy in Wireless Networks

Modern mobile devices are fast, programmable and feature localization and wireless capabilities. These technological advances notably facilitate mobile access to Internet, development of mobile applications and sharing of personal information, such as location information. Cell phone users can for example share their whereabouts with friends on online social networks. Following this trend, the field of ubiquitous computing foresees communication networks composed of increasingly inter-connected wireless devices offering new ways to collect and share information in the future. It also becomes harder to control the spread of personal information. Privacy is a critical challenge of ubiquitous computing as sharing personal information exposes users' private lives. Traditional techniques to protect privacy in wired networks may be inadequate in mobile networks because users are mobile, have short-lived encounters and their communications can be easily eavesdropped upon. These characteristics introduce new privacy threats related to location information: a malicious entity can track users' whereabouts and learn aspects of users' private lives that may not be apparent at first. In this dissertation, we focus on three important aspects of location privacy: location privacy threats, location-privacy preserving mechanisms, and privacy-preservation in pervasive social networks. Considering the recent surge of mobile applications, we begin by investigating location privacy threats of location-based services. We push further the understanding of the privacy risk by identifying the type and quantity of location information that statistically reveals users' identities and points of interest to third parties. Our results indicate that users are at risk even if they access location-based services episodically. This highlights the need to design privacy into location-based services. In the second part of this thesis, we delve into the subject of privacy-preserving mechanisms for mobile ad hoc networks. First, we evaluate a privacy architecture that relies on the concept of mix zones to engineer anonymity sets. Second, we identify the need for protocols to coordinate the establishment of mix zones and design centralized and distributed approaches. Because individuals may have different privacy requirements, we craft a game-theoretic model of location privacy to analyze distributed protocols. This model predicts strategic behavior of rational devices that protects their privacy at a minimum cost. This prediction leads to the design of efficient privacy-preserving protocols. Finally, we develop a dynamic model of interactions between mobile devices in order to analytically evaluate the level of privacy provided by mix zones. Our results indicate the feasibility and limitations of privacy protection based on mix zones. In the third part, we extend the communication model of mobile ad hoc networks to explore social aspects: users form groups called "communities" based on interests, proximity, or social relations and rely on these communities to communicate and discover their context. We analyze using challenge-response methodology the privacy implications of this new communication primitive. Our results indicate that, although repeated interactions between members of the same community leak community memberships, it is possible to design efficient schemes to preserve privacy in this setting. This work is part of the recent trend of designing privacy protocols to protect individuals. In this context, the author hopes that the results obtained, with both their limitations and their promises, will inspire future work on the preservation of privacy

LOCATION SHARING: PRIVACY THREATS AND PROTECTION

In recent years there has been a growing increase in the number of users that use smartphones,tablets, wearable technologies and other devices that users have with them constantly. The capability of these latest generation mobile devices to detect the position of the users has led to the emergence of ad-hoc services as well as geo-aware social networks (GeoSN). Even if the sharing of our locations can enhance many useful services, there are several practical cases that unveil the danger of sharing location indiscriminately. For instance, let\u2019s suppose that a user has just told everyone that he is on vacation (and not at his house): if he adds how long his trip is, then thieves know exactly how much time they have to rob him. Many contributions in the scientific literature have shown how through the location information it is possible to infer several information about the user. It has been shown that it is possible to identify user\u2019s identity, if he is anonymous in the LBS, and, if the user is not anonymous, it is feasible to infer user\u2019s home location, habits and also politic preferences and sexual orientation. The scientific literature reflects this concerns, proposing many contributions that deal with privacy, in general, and location privacy, specifically. This dissertation deals with location privacy in Location Based Services and Geo-Social Networks. The goal is two-fold: on one hand we want to motivate the importance of the location privacy topic by identifying the privacy threats of sharing locations. In particular we study a new privacy threat, the co-location threat, and we further study an already known threat stemming from the use of distance preserving transformations.On the other hand, we want to propose privacy preserving techniques and tools: we propose a novel privacy preserving technique as well as presenting three (spatial and/or temporal) cloaking techniques, specifically designed for privacy techniques in which the privacy is granted by the use of a location\u2019s generalisation

Emerging privacy challenges and approaches in CAV systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions