How private can i be among public users?
Elham Naghizade, James A Bailey, Lars Kulik, Egemen Tanin · 2015
People are increasingly volunteering personal data. Services based on this data rely on a high number of participants and high data quality. Personal data is often seen as private and individuals are more likely to provide such data if they can choose its granularity, e.g., instead of an exact value, they may provide a range. Focusing on spatial crowdsourced data, this work aims to determine whether the common method of coarsening location data of privacy-conscious individuals is an effective approach if fine-grained location data has also been submitted by privacy-apathetic users. We propose a novel inference attack to refine the location of privacy-conscious individuals. Our experiments suggest that even with a dataset that is mostly populated with privacy-conscious users, our technique succeeds with high precision and recall.