Measurement and control of geo-location privacy on Twitter

Abstract

The widespread diffusion of Online Social Networks and Media (OSNEM) has generated a huge amount of users’ personal data. As this data is often publicly available, users’ privacy is at risk. To address this issue, users may control the release of their sensitive data on OSNEM. An example of data that users rarely publish is their location. Besides being a privacy-sensitive information, location is a business-relevant data that third parties, e.g., Location-Based Service (LBS) providers, may be interested to obtain. It is, therefore, of paramount importance to understand to what extent the secrecy of location information can be violated. In this work, we investigate how users can measure the privacy of their geo-location on OSNEM and to control the factors affecting it. We define the privacy of a target user as the geographical distance between her actual unexposed location and the location estimated by an attacker. To measure privacy, we propose a novel deep learning architecture that uncovers a target user’s position based only on the publicly-available locations shared by users on Twitter. Results show that locations can be accurately unveiled for the majority of the users, thus suggesting the need for countermeasures to improve their privacy. To control privacy, we propose data perturbation techniques that users can apply to tune the public exposure of their location, and we show the resulting privacy improvements. To shed light on the factors influencing privacy, we then propose a machine learning model that measures privacy based on several users’ features (e.g., social and behavioral characteristics). Unlike the aforementioned deep learning approach, this model also allows to quantify the impact that each feature has on privacy. We observe that features related to the history of users’ visited locations proved to be the most relevant factors affecting privacy. Finally, we explore potential side effects resulting from the application of data perturbation strategies. In particular, we examine, as a study case, the trade-off between users’ privacy and the effectiveness of a proximity marketing LBS. Results suggest that privacy can be guaranteed while not significantly lowering the effectiveness of the LBS.