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).

Mobile crowdsensing (MCS) has gained popularity for its potential to leverage millions of individual mobile devices to sense, collect, and analyze data instead of deploying thousands of static sensors. As the sensing data become increasingly fine-grained and complicated, there is a tendency to enhance MCS with the edge computing paradigm to reduce time delays and high bandwidth costs. The sensing data may reveal personal information, and thus it is of great significance to preserve the privacy of the participants. However, preserving privacy may hinder the process of handling malicious participants. In this paper, we propose two privacy preserving reputation management schemes for edge computing enhanced MCS to simultaneously preserve privacy and deal with malicious participants. In the basic scheme, a novel reputation value updating method is designed based on the deviations of the encrypted sensing data from the final aggregating result. The basic scheme is efficient at the expense of revealing the deviation value of each participant to the reputation manager. To conquer this drawback, we propose an advanced scheme by updating reputation values utilizing the rank of deviations. Extensive experiments demonstrate that both these two schemes have high cost efficiency and are effective to deal with malicious participants.

Due to the fast-growing usage of wireless devices, cognitive radio networks have been proposed to address the spectrum scarcity problem. As the foundation of their practical applications, designing robust and secure spectrum sensing mechanisms is of great significance. In most existing works, the common control channel (CCC) is assumed to be perfect. However, this assumption may not hold in practice and imperfect CCC makes the existing methods against independent or cooperative data falsification attacks less effective. In this paper, we first analyze the impact of an imperfect CCC on the identification of malicious secondary users under independent and cooperative attacks. To better differentiate honest users and malicious users, a reputation threshold is derived for each secondary user. Based on the obtained reputation threshold, we propose a new reputation-based cooperative spectrum sensing method, which is validated to be robust against attacks under imperfect CCC. Extensive numerical simulations demonstrate the effectiveness of the proposed method.

Virtual reality (VR) apps can harvest a wider range of user data than web/mobile apps running on personal computers or smartphones. Existing law and privacy regulations emphasize that VR developers should inform users of what data are collected/used/shared (CUS) through privacy policies. However, privacy policies in the VR ecosystem are still in their early stages, and many developers fail to write appropriate privacy policies that comply with regulations and meet user expectations. In this paper, we propose VPVet to automatically vet privacy policy compliance issues for VR apps. VPVet first analyzes the availability and completeness of a VR privacy policy and then refines its analysis based on three key criteria: granularity, minimization, and consistency of CUS statements. Our study establishes the first and currently largest VR privacy policy dataset named VRPP, consisting of privacy policies of 11,923 different VR apps from 10 mainstream platforms. Our vetting results reveal severe privacy issues within the VR ecosystem, including the limited availability and poor quality of privacy policies, along with their coarse granularity, lack of adaptation to VR traits and the inconsistency between CUS statements in privacy policies and their actual behaviors. We open-source VPVet system along with our findings at repository https://github.com/kalamoo/PPAudit, aiming to raise awareness within the VR community and pave the way for further research in this field.