Multi-User Behavioral Privacy Filtering for mmWave Radar Sensing

Xiulong Liu, Hankai Liu, Jiaqi Zhang, Xin Xie, Keqiu Li

Abstract: As an advanced technology for non-contact sensing, mmWave radar enables fine-grained measurement of a wide variety of user behaviors. While creating intelligence and convenience, it also concerns behavioral privacy and security, as radar signals contain a wealth of behavioral information. Existing solutions are either incapable of customizable privacy protections or cannot cope with multi-person scenarios. This paper presents a Multi-user behavioral privacy Filter, MuFilter, a data masking system centered on the idea of dimensional signal interference. It determines the sensing signatures that need to be preserved or interfered with based on the sensing services that users want to enable and disable, thereby making targeted tampering on the radar signal. On this basis, we introduce the multi-person tracking technology to allow MuFilter to determine the number of users in unknown scenarios. Moreover, a subspace tampering technique is proposed to ensure that each tampering only affects the target user and not other users, thus supporting personalized privacy protection for multiple users. Experiments show that MuFilter can interfere with targeted behavioral signatures with a 100% success rate, while the degree of impact on other users’ signatures ranges from 0% to 3.85%.

External IDs:dblp:journals/tmc/LiuLZXL25