SoK: Secure Human-centered Wireless Sensing

TL;DR

This work proposes a signal processing pipeline to identify private information leakage and further understand the benefits and tradeoffs of wireless sensing-based inference attacks and defenses, and presents the taxonomy of existing inference attacks and defenses.

Abstract

Human-centered wireless sensing (HCWS) aims to understand the fine-grained environment and activities of a human using the diverse wireless signals around him/her. While the sensed information about a human can be used for many good purposes such as enhancing life quality, an adversary can also abuse it to steal private information about the human (e.g., location and person's identity). However, the literature lacks a systematic understanding of the privacy vulnerabilities of wireless sensing and the defenses against them, resulting in the privacy-compromising HCWS design. In this work, we aim to bridge this gap to achieve the vision of secure human-centered wireless sensing. First, we propose a signal processing pipeline to identify private information leakage and further understand the benefits and tradeoffs of wireless sensing-based inference attacks and defenses. Based on this framework, we present the taxonomy of existing inference attacks and defenses. As a result, we can identify the open challenges and gaps in achieving privacy-preserving human-centered wireless sensing in the era of machine learning and further propose directions for future research in this field.

Figures (5)

• Figure 1: A typical wireless sensing system consists of a transmitter (Tx) and a receiver (Rx), where the Tx transmits wireless signals undergoing the physical environment and the Rx receives wireless signals. The wireless signals may reach the Rx through multiple paths due to reflections of the different objects (e.g., walls) and subjects (e.g., humans) in the physical environment.
• Figure 2: Overview of our proposed signal processing pipeline-based HCWS framework for analyzing and systematizing the existing human-centered wireless sensing.
• Figure 3: Illustration of the prevention strategy. (a) geofencing that can block the wireless signals at the transmitter. (b) Nulling can nullify the signals received by the attacker. (c) Randomization introduces artifacts to the transmitted wireless signals. (d) Jamming can distort the received signals at the attacker. Obfuscation with a phased array or meta surface (e) and full-duplex relay (f) can distort the received wireless signals at the attacker.
• Figure 4: Illustration of the detection strategy. (a) Stimulus uses the generated wireless signals to excite the attacker for detection purposes. (b) Passive sensing can detect the existence of the attacker by overhearing the emanations from him/her. (c) Sensing through the side channel can detect the attacker by sensing the leakage of the undesired side-channel information from the attacker's Rx.
• Figure 5: Adversarial example introduced by the smart surface, phased array, or full-duplex radio can disable the private information inference at the attacker.