000205089 001__ 205089
000205089 005__ 20190909115627.0
000205089 0247_ $$2doi$$a10.5075/epfl-thesis-6515
000205089 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis6515-3
000205089 02471 $$2nebis$$a10381545
000205089 037__ $$aTHESIS
000205089 041__ $$aeng
000205089 088__ $$a6515
000205089 245__ $$bInterdependent Risks and Protection in a Connected World$$aWhen Others Impinge upon Your Privacy
000205089 269__ $$a2015
000205089 260__ $$bEPFL$$c2015$$aLausanne
000205089 336__ $$aTheses
000205089 502__ $$aProf. E. Telatar (président) ; Prof. J.-P. Hubaux (directeur) ; Prof. J. Fellay,  Prof. A. Juels,  Prof. R. Molva (rapporteurs)
000205089 520__ $$aPrivacy 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.[...]
000205089 6531_ $$ainterdependent privacy
000205089 6531_ $$agenomic privacy
000205089 6531_ $$aonline social networks (OSNs)
000205089 6531_ $$aincentives
000205089 6531_ $$acooperation
000205089 6531_ $$aBayesian inference
000205089 6531_ $$agraphical models
000205089 6531_ $$aobfuscation mechanism
000205089 6531_ $$agame theory
000205089 6531_ $$aMarkov chains
000205089 700__ $$0242758$$g166947$$aHumbert, Mathias
000205089 720_2 $$aHubaux, Jean-Pierre$$edir.$$g105427$$0240456
000205089 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/205089/files/EPFL_TH6515.pdf$$s2445997
000205089 909C0 $$xU10426$$pLDS$$0252452
000205089 909CO $$qDOI2$$qIC$$qGLOBAL_SET$$pthesis$$pthesis-bn2018$$pthesis-public$$pDOI$$ooai:infoscience.tind.io:205089
000205089 917Z8 $$x108898
000205089 917Z8 $$x108898
000205089 917Z8 $$x108898
000205089 917Z8 $$x108898
000205089 918__ $$dEDIC2005-2015$$cISC$$aIC
000205089 919__ $$aLCA1
000205089 920__ $$b2015$$a2015-3-13
000205089 970__ $$a6515/THESES
000205089 973__ $$sPUBLISHED$$aEPFL
000205089 980__ $$aTHESIS