Behavioral biometrics based continuous authentication has been proven to be an effective supplement to traditional one-time authentication schemes ( like passwords), and it can continuously authenticate users throughout the session. Currently, most continuous authentication models leverage deep learning techniques to learn smartphone users' behavioral patterns from behavioral biometric data, and have made remarkable progress. However, training deep learning based continuous authentication models requires enormous computing power, which leads to resource-constrained mobile platforms relying on powerful cloud servers. Cloud servers need access to behavioral biometric data for training and inference, as this mayraise privacy concerns. Existing deep learning based continuous authentication schemes pay more attention to authenticationperformance, but ignore theprotectionof behavioral biometric data. To solve this issue, we present a privacy-preserving continuous authentication scheme based on secret sharing secure multi-party computation (MPC). More specifically, we secretly share behavioral biometric data among two cloud servers that train continuous authentication systems on joint data using two-party computation (2 PC) without compromising data privacy. Further, we evaluate the practical feasibility of our proposed privacy-preserving scheme on two realistic privacy-preserving continuous authentication models, which are constructed with deep learning and traditional machine learning techniques, respectively. Extensive experiments demonstrate the effectiveness of our two privacy-preserving continuous authentication models.

Passenger counting is crucial for public transport vehicle scheduling and traffic capacity evaluation. However, most existing methods are either costly or with low counting accuracy, leading to the recent use of Wi-Fi signals for this purpose. In this paper, we develop an efficient edge computing-based passenger counting system consists of multiple Wi-Fi receivers and an edge server. It leverages channel state information (CSI) and received signal strength indicator (RSSI) to facilitate the collaboration among multiple receivers. Specifically, we design a novel CSI feature fusion module called Adaptive RSSI-weighted CSI Feature Concatenation, which integrates locally extracted CSI and RSSI features from multiple receivers for information fusion at the edge server. Performance of our proposed system is evaluated using a real-world dataset collected from a double-decker bus in Hong Kong, with up to 20 passengers. The experimental results reveal that our system achieves an average accuracy and F1-score of over 94 %, surpassing other cooperative sensing baselines by at least 2.27 % in accuracy and 2.34 % in F1-score.

Recent studies in Wi-Fi sensing have demonstrated the potential of channel state information (CSI) for indoor crowd counting. However, their practical application remains limited by constraints in sensing range and robustness. While multi-transceiver setups have shown promise in enhancing performance, the impact of transceiver placement strategies on sensing effectiveness remains underexplored. In this work, we systematically investigate how sensor placement affects sensing performance in stationary crowd counting. We extend two foundational models, the Fresnel zone and sensing-signal-to-noise ratio (SSNR) formulations, originally designed for single-target, single-link scenarios, and generalize them to characterize spatial sensing quality in multi-target, multi-link environments. Based on this theoretical foundation, we propose a deployment evaluation model that quantifies sensing performance using a normalized metric termed the Regional Sensing Quality (RSQ), and enables direct comparison among different transceiver topologies. Experimental results across diverse environments show that optimizing deployment can improve crowd counting accuracy by up to 20.48%, with our system achieving 98.14% accuracy for up to 20 individuals. This work provides the first framework that integrates theoretical modeling and practical validation to guide transceiver deployment for robust and scalable CSI-based stationary crowd sensing.