  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.
