By the end of 2013, the number of internet-connected mobile devices is expected to exceed that of humans. Omnipresent and context-aware, mobile devices enable people to communicate and exchange data anytime and almost anywhere. The myriad of ”digital footprints” that mobile devices leave can be used to infer a large amount of personal information about their owners. For instance, the IP address can be used to infer a coarse-grained location of the device, the temporary identifiers used in cellular networks can be used to track people’s whereabouts and infer numerous personal details. Similarly, online social networks often force members to share some personal information with all other users or service providers, de facto exposing users to unwanted profiling by advertisement companies and other private and state agencies. At each layer of the network stack, there is some information that can be used to track and profile mobile users; it is therefore crucial to investigate the privacy challenges present at different layers and design privacy protection mechanisms that work across these layers. In this thesis, we take a top-down approach on privacy in mobile networks by (i) studying the issues present in different network layers – the application, IP and link layers – and (ii) by proposing protection mechanisms and quantifying the extent of private information leakage. First, we look at the application layer, where we design protocols to protect users’ personal data from third-party entities and other unauthorized users. In particular, we focus on two relevant problems: meeting scheduling and optimal meeting location determination. For these two problems, we propose and evaluate privacy-preserving protocols that are both practical and more efficient than the existing approaches. Second, we study the privacy challenges that arise in the network and link layers, by quantifying the exposure of social community information in a large on-campus experiment. In addition, we evaluate the effect of the reconstructed community information on the inference of social ties among the participants to the experiment. For the first time in the same experiment, we compare the reconstruction accuracy of a realistic eavesdropper, who has only access to packet headers exchanged among the mobile devices, with that of a malicious application or entity that has access to the on-device data. Third, by taking a cross-layer approach, we design and evaluate a mobile social-networking application that enables users to share different kinds of personal information in a privacy-aware and inobtrusive way. In particular, we show how existing information-sharing policies are ineffective in correctly predicting users actual sharing behavior; then, based on a probabilistic decision-making framework, we demonstrate how machine learning can be used to automatically decide whether and how much to share – based on the users’ context and past behavior. Our results indicate that the proposed machine-learning-based approach is more comprehensive and practical than existing automated solutions and, at the same time, it is more effective than fixed policy-based rules – all while requiring a minimal effort from the users.