# A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
## 4 Defenses for WiFi Sensing
### Passive Defense  
Passive defense refers to protective measures adopted by external parties, such as those using the sensing data or third-party entities [58; 59; 86; 87; 88]. These defenses operate without requiring direct involvement from the sensing target, focusing on safeguarding the  
Figure 6: Illustration of the high-level idea of IRShield [56].  
integrity and confidentiality of the sensed information through external mechanisms. By intercepting potential threats or vulnerabilities before they can affect the target, passive defense systems provide an additional layer of security. As the sophistication of wireless attacks continues to grow, passive defense strategies will become increasingly vital in maintaining the privacy and security of wireless sensing systems without relying on direct user intervention.  
Focusing on using WiFi sensing for crowd monitoring (e.g., people counting for safety and security management), [PERSON] et al. [88] also paid attention to the privacy issue. Specifically, a fingerprint-based scheme was proposed to leverage probe request messages emitted by smart devices as a proxy for crowd monitoring. Meanwhile, this solution exploits Bloom filters to ensure formal deniability, aligning with the stringent requirements set forth by regulations like the European GDPR. In that case, this scheme not only addresses the essential task of crowd monitoring but also incorporates advanced privacy-preserving mechanisms, offering a comprehensive approach to managing crowds.  
Considering serious privacy concerns raised by cloud-based indoor localization services, [PERSON] et al. [87] proposed a privacy-preserving indoor positioning scheme for WiFi localization based on inner product encryption (IPE) in a cloud environment. They also designed a Bloom filter, which is constructed with locality sensitive hashing, to map WiFi fingerprints from Euclidean to inner product space with the distance relationships maintained for converting the location estimation to inner product calculations. Note that IPE can protect the user's fingerprint and database information held by the positioning service provider. Fingerprint similarity as determined by the inner product is decrypted on the cloud to retrieve the closest encrypted location coordinates for users. This scheme can effectively ensure sensing security while not significantly degrading the localization accuracy.  
Similarly, PriLA [86] also noted the privacy concerns of location authentication with WiFi technologies. Thus, PriLA constructs a privacy-preserving location authentication protocol that facilitates location authentication without compromising users' location privacy in WiFi networks. Specifically, PriLA leverages carrier frequency offset (CFO) to secure wireless transmission between the mobile user and the AP, meanwhile authenticating the reported locations without leaking the exact location information based on the coarse-grained location proximity being extracted from users' multipath profiles.  
PrivateBus [58] focuses on another novel scenario: public WiFi spots in modern transportation systems (e.g., buses). PrivateBus incorporates two models for uniqueness analyses and sensing information protection. On one hand, a PB-FIND model is utilized to identify the probability that a user can be uniquely re-identified from leaked information, including users' finger traces (i.e., connection URL and domain), foot traces (i.e., locations), and hybrid traces. On the other hand, a PB-HIDE model protects users from potentially leaked information.