Kostas Drakonakis (FORTH, Greece), Panagiotis Ilia (FORTH, Greece), Sotiris Ioannidis (FORTH, Greece), Jason Polakis (University of Illinois at Chicago, USA)

The exposure of location data constitutes a significant privacy risk to users as it can lead to de-anonymization, the inference of sensitive information, and even physical threats. In this paper we present LPAuditor, a tool that conducts a comprehensive evaluation of the privacy loss caused by public location metadata. First, we demonstrate how our system can pinpoint users’ key locations at an unprecedented granularity by identifying their actual postal addresses. Our evaluation on Twitter data highlights the effectiveness of our techniques which outperform prior approaches by 18.9%-91.6% for homes and 8.7%-21.8% for workplaces. Next we present a novel exploration of automated private information inference that uncovers “sensitive” locations that users have visited (pertaining to health, religion, and sex/nightlife). We find that location metadata can provide additional context to tweets and thus lead to the exposure of private information that might not match the users’ intentions.

We further explore the mismatch between user actions and information exposure and find that older versions of the official Twitter apps follow a privacy-invasive policy of including precise GPS coordinates in the metadata of tweets that users have geotagged at a coarse-grained level (e.g., city). The implications of this exposure are further exacerbated by our finding that users are considerably privacy-cautious in regards to exposing precise location data. When users can explicitly select what location data is published, there is a 94.6% reduction in tweets with GPS coordinates. As part of current efforts to give users more control over their data, LPAuditor can be adopted by major services and offered as an auditing tool that informs users about sensitive information they (indirectly) expose through location metadata.

View More Papers

Cybercriminal Minds: An investigative study of cryptocurrency abuses in...

Seunghyeon Lee (KAIST, S2W LAB Inc.), Changhoon Yoon (S2W LAB Inc.), Heedo Kang (KAIST), Yeonkeun Kim (KAIST), Yongdae Kim (KAIST), Dongsu Han (KAIST), Sooel Son (KAIST), Seungwon Shin (KAIST, S2W LAB Inc.)

Read More

Anonymous Multi-Hop Locks for Blockchain Scalability and Interoperability

Giulio Malavolta (Friedrich-Alexander University Erlangen-Nürnberg), Pedro Moreno Sanchez (TU Wien), Clara Schneidewind (TU Wien), Aniket Kate (Purdue University), Matteo Maffei (TU Wien)

Read More

A Treasury System for Cryptocurrencies: Enabling Better Collaborative Intelligence

Bingsheng Zhang (Lancaster University), Roman Oliynykov (IOHK Ltd.), Hamed Balogun (Lancaster University)

Read More

Life after Speech Recognition: Fuzzing Semantic Misinterpretation for Voice...

Yangyong Zhang (Texas A&M University), Lei Xu (Texas A&M University), Abner Mendoza (Texas A&M University), Guangliang Yang (Texas A&M University), Phakpoom Chinprutthiwong (Texas A&M University), Guofei Gu (Texas A&M University)

Read More