The rapid development of the smart healthcare system makes the early-stage detection of dementia disease more user-friendly and affordable. However, the main concern is the potential serious privacy leakage of the system. In this article, we take Alzheimer's disease (AD) as an example and design a convenient and privacy-preserving system named ADDetector with the assistance of Internet of Things (IoT) devices and security mechanisms. Particularly, to achieve effective AD detection, ADDetector only collects user's audio by IoT devices widely deployed in the smart home environment and utilizes novel topic-based linguistic features to improve the detection accuracy. For the privacy breach existing in data, feature, and model levels, ADDetector achieves privacy-preserving by employing a unique three-layer (i.e., user, client, cloud, etc.) architecture. Moreover, ADDetector exploits federated learning (FL) based scheme to ensure the user owns the integrity of raw data and secure the confidentiality of the classification model and implement differential privacy (DP) mechanism to enhance the privacy level of the feature. Furthermore, to secure the model aggregation process between clients and cloud in FL-based scheme, a novel asynchronous privacy-preserving aggregation framework is designed. We evaluate ADDetector on 1010 AD detection trials from 99 health and AD users. The experimental results show that ADDetector achieves high accuracy of 81.9% and low time overhead of 0.7 s when implementing all privacy-preserving mechanisms (i.e., FL, DP, and cryptography-based aggregation).

In this study, we present WindTalker, a novel and practical keystroke inference framework that can be used to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from an observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). An attacker can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's password input. Compared with the previous keystroke inference approaches, WindTalker neither deploys external equipment physically close to the target device nor compromises the target device. Instead, it employs a more practical setting by deploying a free public WiFi hotspot and collects the CSI data from the target device as long as the device is connected to the hotspot. In addition, to improve inference accuracy and efficiency, it analyzes the WiFi traffic to selectively collect CSI only for the sensitive period where password entering occurs. WindTalker can be implemented without the requirement of visually seeing the target device, or installing any malware on the device. We tested Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. Furthermore, we proposed a novel CSI obfuscation countermeasure to thwart the inference attack. The evaluation results show that the performance of WindTalker can be dramatically reduced by adopting the proposed countermeasures.

With the development of 5 G and other communication techniques, the space-air-ground integrated network (SAGIN) is regarded as a promising solution to provide wide-range, cost-effective, and real-time wireless access. Unmanned Aerial Vehicle (UAV) Tracking plays a crucial role in guaranteeing the performance of SAGIN. However, UAV tracking still faces a series of challenges in the real-world deployment, especially in the case of the presence of malicious attackers, who may launch a series of attacks ( e.g. , message spoofing attack, routing misbehavior attack) to disrupt the systems. In this study, to enhance the security and efficiency of SAGIN when conducting object tracking, we propose a novel and secure object tracking system named SecTracker to overcome the emerging security problems in the SAGIN. Focusing on the unmanned aerial vehicle (UAV) tracking, we design the Local Voting based Detection Module to defend the message spoofing attack and implement the Routing Evidence based Detection Module to defend the routing misbehavior attack. Besides, efficiency enhancement mechanisms ( i.e. , probabilistic detection algorithm and game theory based algorithm) are proposed in this paper to improve the efficiency of SecTracker . Considering the above-mentioned attacks, the overall tracking accuracy is improved from 88.16% in existing schemes to 96.64% in SecTracker , which demonstrates the effectiveness of the proposed system.

As location data have been increasingly adopted in location-based advertising (LBA), revealing locations to untrusted service providers has raised severe privacy concerns. Recent studies propose obfuscation mechanisms built upon geo-indistinguishability (geo-IND) to provide formal privacy guarantee. Unfortunately, due to the high degree of spatiotemporal regularity in human mobility pattern, the privacy cost will be unacceptably high in this situation, leading to accurate inference of user real locations. In this study, we identify this privacy risk in LBA scenarios under long-term and multi-platform assumption. We demonstrate an attacker can infer 75%∼90% of top-1 locations within a range of only 200 meters. To address it, we propose PrivLocAd , a novel system which can provide longitudinal privacy guarantee. The novelty of PrivLocAd stems from a novel surrogate-based obfuscation, which generates multiple surrogate locations to improve the privacy-utility trade-off. In addition, two novel obfuscation mechanisms, the two-stage Gaussian and multi-level surrogate generation mechanism in charge of surrogate generation can achieve the longitudinal privacy guarantee in intra- and inter-platform condition respectively. Our experimental results demonstrate PrivLocAd is able to defend against the attack, which reduces the inference rate to less than 1% of user top-1 locations in the 200 meter range.

Virtual reality (VR), offering 3D visuals and stereophonic sounds, significantly enhances users’ immersive experiences and has become a milestone in the era of the metaverse. However, due to the limited battery capacity of VR devices, it is common for users to rely on charging cables, which serve the dual purpose of power supply and audio output, to recharge their VR devices while in use. In this study, we propose an inconspicuous and stealthy side channel attack, coined as LineTalker, which can unveil visual-related and audio-related activities from VR devices during the charging process. The insight behind LineTalker is rooted in the observation that visual-related activities (e.g., 3D image rendering) are power-intensive and result in fluctuations in the current strength of the cable’s power supply line, which can be leveraged as side channel information. Similarly, audio-related activities (e.g., playing music) leave traces on the cable’s audio output line. Rather than providing a user with a compromised charging cable (i.e., embedding a current sensor) to measure the current strength, to make the attack less conspicuous, LineTalker employs the Hall effect to indirectly access side channel information. This is achieved by capturing magnetic signals using a Hall sensor placed near the target cable in a contactless manner. Experimental results demonstrate that LineTalker achieves an overall accuracy of 94.60% and 64.38% in inferring user activities in VR devices with intrusive and non-intrusive attack manners, respectively.

By providing users with an immersive visual and acoustic experience, virtual reality (VR) serves as a foundational technique for the emerging metaverse. One of the most promising aspects of VR is its ability to protect users’ identities by transforming their physical appearances into avatars with arbitrary appearances in the virtual world. However, the increasing threat of de-anonymization attacks that seek to reveal users’ identities poses significant privacy risks. We propose AvatarHunter, a non-intrusive and user-unaware de-anonymization attack leveraging victims’ inherent movement signatures. AvatarHunter discreetly collects the avatar's gait information by recording videos in the VR scenario without requiring any permissions. Notably, we designed a Unity-based feature extractor that maintains the avatar's movement signature while enabling AvatarHunter to be resistant to changes in the avatar's appearance. We conduct real-world experiments on VRChat to evaluate AvatarHunter's effectiveness. The results demonstrate that in commercial settings, AvatarHunter achieves attack success rates (ASR) of 92.1% and 66.9% in closed-world and open-world avatar scenarios, respectively, significantly surpassing existing benchmarks. Additionally, simulations using an open-source dataset confirm that AvatarHunter can attain over 78% ASR in full-body tracking scenarios. Finally, we discuss several countermeasures and implement an obfuscation mechanism during the avatar rendering phase, significantly reducing the ASR.