Privacy is defined as the right to control, edit, manage, and delete information about oneself and decide when, how, and to what extent this information is communicated to others. Therefore, every person should ideally be empowered to manage and protect his own data, individually and independently of others. This assumption, however, barely holds in practice, because people are by nature biologically and socially interconnected. An individual's identity is essentially determined at the biological and social levels. First, a person is biologically determined by his DNA, his genes, that fully encode his physical characteristics. Second, human beings are social animals, with a strong need to create ties and interact with their peers. Interdependence is present at both levels. At the biological level, interdependence stems from genetic inheritance. At the social level, interdependence emerges from social ties. In this thesis, we investigate whether, in today's highly connected world, individual privacy is in fact achievable, or if it is almost impossible due to the inherent interdependence between people. First, we study interdependent privacy risks at the social level, focusing on online social networks (OSNs), the digital counterpart of our social lives. We show that, even if an OSN user carefully tunes his privacy settings in order to not be present in any search directory, it is possible for an adversary to find him by using publicly visible attributes of other OSN users. We demonstrate that, in OSNs where privacy settings are not aligned between users and where some users reveal a (even limited) set of attributes, it is almost impossible for a specific user to hide in the crowd. Our navigation attack complements existing work on inference attacks in OSNs by showing how we can efficiently find targeted profiles in OSNs, which is a necessary precondition for any targeted attack. Our attack also demonstrates the threat on OSN-membership privacy. Second, we investigate upcoming interdependent privacy risks at the biological level. More precisely, due to the recent drop in costs of genome sequencing, an increasing number of people are having their genomes sequenced and share them online and/or with third parties for various purposes. However, familial genetic dependencies induce indirect genomic privacy risks for the relatives of the individuals who share their genomes. We propose a probabilistic framework that relies upon graphical models and Bayesian inference in order to formally quantify genomic privacy risks. Then, we study the interplay between rational family members with potentially conflicting interests regarding the storage security and disclosure of their genomic data. We consider both purely selfish and altruistic behaviors, and we make use of multi-agent influence diagrams to efficiently derive equilibria in the general case where more than two relatives interact with each other. We also propose an obfuscation mechanism in order to reconcile utility with privacy in genomics, in the context where all family members are cooperative and care about each other's privacy. Third, we study privacy-enhancing systems, such as anonymity networks, where users do not damage other users' privacy but are actually needed in order to protect privacy. In this context, we show how incentives based on virtual currency can be used and their amount optimized in order to foster cooperation between users and eventually improve everyone's privacy.[...]