This paper highlights the main data collection fields, taking a user’s view on the risks and tradeoffs regarding online data collection and privacy. | International Journal of Computer Networks and Communications Security C , , NOVEMBER 2013, 237–250 Available online at: ISSN 2308-9830 N C S Data Privacy: An End-User Perspective Esma Aïmeur1, Gilles Brassard2, Jonathan Rioux3 1, 2, 3 Département d’informatique et de recherche opérationnelle, Université de Montréal E-mail: {aimeur,brassard,riouxjon}@ ABSTRACT Online privacy has become a raising concern for digital citizens within the past few years. As users of the Internet, we are in an uncomfortable situation regarding the protection of our online data. We share, tweet, like and follow at an ever-increasing rate, while at the same time getting more aware of the possible dangers of privacy breach or identity theft. Is it possible to navigate on the web while being sure we’re not being spied on? This paper highlights the main data collection fields, taking a user’s view on the risks and tradeoffs regarding online data collection and privacy. Keywords: Privacy, User Experience, Data Collection, Social Media, Identity Theft. 1 INTRODUCTION The Internet has quickly become a central part of our lives. We now spend hours browsing, writing emails, frequenting social media and sharing with loved ones and people all over the globe. We made great progress bringing more and more people online and the web isn’t a haven for scientists anymore. We’re now witnessing a democratization of online access, combined with a focus on web technologies. Websites are trying to tailor themselves to their customers, gathering and using the information they are providing in order to offer a differentiated product. Most people are aware of their browser’s history and cookies, but with the rise of single-login, geolocation and online profiles, the boundaries are getting blurrier. Companies are collecting data at an exponential rate. For example, Facebook is investing massively in new technologies to deal with never seen before amounts of data:
