Group-In: Group Inference from Wireless Traces of Mobile Devices

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 202

Location Privacy Protection in the Mobile Era and Beyond

As interconnected devices become embedded in every aspect of our lives, they accompany many privacy risks. Location privacy is one notable case, consistently recording an individual’s location might lead to his/her tracking, fingerprinting and profiling. An individual’s location privacy can be compromised when tracked by smartphone apps, in indoor spaces, and/or through Internet of Things (IoT) devices. Recent surveys have indicated that users genuinely value their location privacy and would like to exercise control over who collects and processes their location data. They, however, lack the effective and practical tools to protect their location privacy. An effective location privacy protection mechanism requires real understanding of the underlying threats, and a practical one requires as little changes to the existing ecosystems as possible while ensuring psychological acceptability to the users. This thesis addresses this problem by proposing a suite of effective and practical privacy preserving mechanisms that address different aspects of real-world location privacy threats. First, we present LP-Guardian, a comprehensive framework for location privacy protection for Android smartphone users. LP-Guardian overcomes the shortcomings of existing approaches by addressing the tracking, profiling, and fingerprinting threats posed by different mobile apps while maintaining their functionality. LP-Guardian requires modifying the underlying platform of the mobile operating system, but no changes in either the apps or service provider. We then propose LP-Doctor, a light-weight user-level tool which allows Android users to effectively utilize the OS’s location access controls. As opposed to LP-Guardian, LP-Doctor requires no platform changes. It builds on a two year data collection campaign in which we analyzed the location privacy threats posed by 1160 apps for 100 users. For the case of indoor location tracking, we present PR-LBS (Privacy vs. Reward for Location-Based Service), a system that balances the users’ privacy concerns and the benefits of sharing location data in indoor location tracking environments. PR-LBS fits within the existing indoor localization ecosystem whether it is infrastructure-based or device-based. Finally, we target the privacy threats originating from the IoT devices that employ the emerging Bluetooth Low Energy (BLE) protocol through BLE-Guardian. BLE-Guardian is a device agnostic system that prevents user tracking and profiling while securing access to his/her BLE-powered devices. We evaluate BLE-Guardian in real-world scenarios and demonstrate its effectiveness in protecting the user along with its low overhead on the user’s devices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138563/1/kmfawaz_1.pd