Federated Learning (FL) is a transformative paradigm for training global machine learning models using decentralized datasets hosted by edge or client devices, without requiring centralized data aggregation. This makes FL particularly valuable for privacy-sensitive applications deployed in edge-cloud environments. However, FL is vulnerable to Byzantine attacks, where malicious clients provide falsified local model updates to compromise the performance of the global model. Such vulnerabilities are especially problematic in heterogeneous and resource-constrained edge-cloud systems, where ensuring trust across distributed clients is a significant challenge. This paper introduces a novel Byzantine-Robust FL method designed to address these challenges in edge-cloud computing scenarios. Our method leverages a central server equipped with a small clean dataset as an initial root of trust to evaluate the trustworthiness of client updates. Unlike prior approaches, our method iteratively builds a dynamic set of trusted clients, gradually incorporating their updates into the training process to refine the global model. This dynamic trust mechanism reduces reliance on the quality of the initial clean dataset, ensuring robustness even in the presence of a significant number of malicious clients. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach in achieving high model accuracy and maintaining robustness against Byzantine attacks. Our method is particularly well-suited for edge-cloud environments, addressing critical challenges such as resource constraints, distributed learning management, and the reliability of collaborative AI systems.

Location Based Services (LBSs) have become increasingly popular in the past decade, allowing mobile users to access location-dependent information and services. To protect user privacy while using LBSs, various Location Privacy Protection Mechanisms (LPPMs) have been proposed that obfuscate users' true locations through random perturbation. However, adversaries can still exploit the temporal correlation between a user's locations in multiple LBS queries to improve inference accuracy. In this paper, we introduce a novel location inference attack that strikes a good balance between inference accuracy and computational complexity by effectively exploiting temporal correlation. Simulation studies using synthetic and real datasets confirm the advantages of our proposed attack.

When been threatened by hijacking or suicide-bypilots, the airliner may either crash itself or be shot down due to the potential of the suicide attack. There exist some solutions that allow air traffic controllers or federal agents to take over pilots' authority in the emergency. Though rarely, an air traffic controller may abuse this privilege to mishandle airliners that leads to catastrophic events. In this paper, to mitigate such risks, we propose a multisecret value access control framework based on new designed and existing cryptographic techniques such as XOR-based secret sharing schemes (SSSs). It not only satisfies the efficiency requirement but also assures that each nation owns a unique secret value. We further develop and implement the XOR-based SSS on Linux system. Both experimental results and performance evaluation demonstrate that our solution is not only efficient and bust also secure by design for the multinational air traffic management.

Energy-wasting behavior is a big concern as it wastes around 1/3 of all energy consumption in buildings. Current solutions to address this issue either rarely offer privacy preservation or cannot satisfy occupants' comfort in an acceptable level. In this paper, we first propose an energy-wasting behavior detection platform. Based on that, we address a couple of privacy-related challenges in our platform through utilizing functional encryption to hide video data and by introducing noise disturbance which is mixed with metering data e.g. A/C power consumption signatures. In a privacy framework we proposed, we further quantify the privacy leakage by a set of theoretical models e.g. Hidden Markov model and differential privacy. Since our paper is still at its start phase, we plan to further extend our privacy evaluation model, assess privacy leakage on real-world dataset and accomplish experiments and we wish it could inspire colleagues' interests in this area.

If Software-Defined Networking (SDN) is deployed on the ambulance, the doctor / care giver could remotely diagnose / take care of the patient after the patients are onboard. The patient get more chance to survive. After analyzing how to present patients earlier care, this paper's goals are to (1) enable an ambulance to transport the patient to hospitals as early as possible (2) ensure the paramedic provide care earlier via SDN. A set of graphic algorithms have been proposed and the experimental results demonstrate the feasibility of our solution.

Current popular schemes e.g. homomorphic cryptography are extensively deployed to preserve privacy in a limited level but without a formal privacy model, we can neither offer privacy guarantee nor quantify the privacy loss. In this paper, we raise a few privacy-related questions, one after another, with the e-Lock state changes in a smart home as an example. In a novel privacy framework we proposed, the questions are partially addressed with the utilization of a set of theoretical models e.g. hidden markov model, differential privacy and information flow with belief. Since our paper is still at its start phase, we plan to accomplish the framework and wish can inspire colleagues' interests in this area.

The recently emerging advanced metering infrastructure AMI is envisioned to be one of the most prominent features of smart grid. Security, especially authentication, is crucial for the success of large-scale AMI deployment. Unfortunately, AMI's natural requirements-efficiency, scalability, fault diagnoses, and reliability-cannot be fully satisfied by existing authentication schemes. To validate the delay-tolerant AMI metering data, we present a new authentication architecture that boosts the utilization of a set of efficient authentication schemes. Meanwhile, in case of authentication failures, fault diagnoses are highly demanded by AMI. Thus, we develop a few corresponding fault diagnosis algorithms that precisely pinpoint the authentication failure with lightweight computational cost. We implement our system on emulated smart meters and commodity servers. Experimental results on real-world scenarios demonstrate the feasibility of our system; it merely incurs substantially lighter overheads than those by the existing schemes, while it can effectively address the formidable authentication challenges in AMI. Copyright © 2014 John Wiley & Sons, Ltd.

To address recently emerging concerns on privacy violations, this paper investigates possible sensitive information leakages and analyzes potential privacy threats in the automatic appliance control (AAC) application, which is one of the handiest applications in smart grids and one of the earliest examples in Internet of Things (IoT). Without an effective and consistent privacy preservation mechanism, the adversary can capture, model, and divulge customers' behavior, activities, and personal information at almost every level of society. Based on a set of existing cryptographic primitives, we propose an attribute-based encryption (ABE) key management variant and we also design and implement a fine-grained protocol named privacy preservation protocol (P3). We further present a practical automatic appliance control (AAC) system based on that protocol, and shows that it can fulfill the smart grid's requirements in privacy preservation. Experimental results demonstrate that our protocol merely incurs a substantially light overhead on the AAC application, yet is able to address and solve the formidable privacy challenges both customers and utility companies are facing.