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Abstract We present the design, implementation and evaluation of a system, called MATRIX, developed to protect the privacy of mobile device users from location inference and sensor side-channel attacks. MATRIX gives users control and visibility over location and sensor (e.g., Accelerometers and Gyroscopes) accesses by mobile apps. It implements a PrivoScope service that audits all location and sensor accesses by apps on the device and generates real-time notifications and graphs for visualizing these accesses; and a Synthetic Location service to enable users to provide obfuscated or synthetic location trajectories or sensor traces to apps they find useful, but do not trust with their private information. The services are designed to be extensible and easy for users, hiding all of the underlying complexity from them. MATRIX also implements a Location Provider component that generates realistic privacy-preserving synthetic identities and trajectories for users by incorporating traffic information using historical data from Google Maps Directions API, and accelerations using statistical information from user driving experiments. These mobility patterns are generated by modeling/solving user schedule using a randomized linear program and modeling/solving for user driving behavior using a quadratic program. We extensively evaluated MATRIX using user studies, popular location-driven apps and machine learning techniques, and demonstrate that it is portable to most Android devices globally, is reliable, has low-overhead, and generates synthetic trajectories that are difficult to differentiate from real mobility trajectories by an adversary.
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This content will become publicly available on January 1, 2026
BehaVR: User Identification Based on VR Sensor Data
Virtual reality (VR) platforms enable a wide range of applications, however, pose unique privacy risks. In particular, VR devices are equipped with a rich set of sensors that collect personal and sensitive information (e.g., body motion, eye gaze, hand joints, and facial expression). The data from these newly available sensors can be used to uniquely identify a user, even in the absence of explicit identifiers. In this paper, we seek to understand the extent to which a user can be identified based solely on VR sensor data, within and across real-world apps from diverse genres. We consider adversaries with capabilities that range from observing APIs available within a single app (app adversary) to observing all or selected sensor measurements across multiple apps on the VR device (device adversary). To that end, we introduce BehaVR, a framework for collecting and analyzing data from all sensor groups collected by multiple apps running on a VR device. We use BehaVR to collect data from real users that interact with 20 popular real-world apps. We use that data to build machine learning models for user identification within and across apps, with features extracted from available sensor data. We show that these models can identify users with an accuracy of up to 100%, and we reveal the most important features and sensor groups, depending on the functionality of the app and the adversary. To the best of our knowledge, BehaVR is the first to analyze user identification in VR comprehensively, i.e., considering all sensor measurements available on consumer VR devices, collected by multiple real-world, as opposed to custom-made, apps.
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- PAR ID:
- 10585583
- Publisher / Repository:
- The journal Proceedings on Privacy Enhancing Technologies (PoPETs)
- Date Published:
- Journal Name:
- Proceedings on Privacy Enhancing Technologies
- Volume:
- 2025
- Issue:
- 1
- ISSN:
- 2299-0984
- Page Range / eLocation ID:
- 399 to 419
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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