Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems
Localization services for wireless devices play an increasingly important role in our daily lives and a plethora of emerging services and applications already rely on precise position information. Widely used on-device positioning methods, such as the global positioning system, enable accurate outdoor positioning and provide the users with full control over what services and applications are allowed to access their location information. In order to provide accurate positioning indoors or in cluttered urban scenarios without line-of-sight satellite connectivity, powerful off-device positioning systems, which process channel state information (CSI) measured at the infrastructure base stations or access points with deep neural networks, have emerged recently. Such off-device wireless positioning systems inherently link a user's data transmission with its localization, since accurate CSI measurements are necessary for reliable wireless communication -- this not only prevents the users from controlling who can access this information but also enables virtually everyone in the device's range to estimate its location, resulting in serious privacy and security concerns. We therefore propose on-device attacks against off-device wireless positioning systems in multi-antenna orthogonal frequency-division multiplexing systems while remaining standard compliant and minimizing the impact on quality-of-service, and we demonstrate their efficacy using real-world measured datasets for cellular outdoor and wireless LAN indoor scenarios. We also investigate defenses to counter such attack mechanisms, and we discuss the limitations and implications on protecting location privacy in existing and future wireless communication systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Outdoor PositioningSimilar Papers 제목 키워드 기반
Security Matters: A Survey on Adversarial Machine Learning
Adversarial machine learning is a fast growing research area, which considers the scenarios when machine learning systems may face potential adversarial attackers, who intentionally synthesize input data to make a well-t…
BIG-bench Machine LearningDeep LearningSurveyMemoryless Techniques and Wireless Technologies for Indoor Localization with the Internet of Things
In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of Indoor Positioning Systems (IPS) and Location Based Services (LBS). When designing an IPS, a popular approach …
Indoor LocalizationSmartFPS: Neural Network based Wireless-inertial fusion positioning system
The current fusion positioning systems are mainly based on filtering algorithms, such as Kalman filtering or particle filtering. However, the system complexity of practical application scenarios is often very high, such …
Transfer LearningRange Variation Monitoring with Wireless Two-Way Interferometry (Wi-Wi)
We demonstrated a simple technique for monitoring range variation with millimeter-precision between two remote sites using off-the-shelf wireless communication modules. The need for the flexible positioning of wireless d…
Vocal Bursts Valence PredictionA Multi-band Solution for Interacting with Energy-Neutral Devices
RF Wireless Power Transfer (WPT) emerges as a technology for charging autonomous devices, enabling simultaneous power and information transfer. However, with increasing distance, single-input, single-channel rectenna sys…