In this paper, we propose an effective blockchain-enabled information management framework, named Smart Blockchain-based Web 3.0 (SBW 3.0). Our framework aims to handle information within Web 3.0 efficiently, enhance data security and privacy, create new revenue streams, and encourage users to contribute valuable information to websites. To this end, SBW 3.0 employs blockchain technology and smart contracts to manage the decentralized data collection in Web 3.0. Moreover, we introduce a robust consensus mechanism grounded in Delegated Proof-of-Stake (DPoS) to reward user contributions. Furthermore, we develop a non-cooperative game model to examine user behavior in this context and conduct thorough analysis to prove the uniqueness of the Nash equilibrium in our proposed system. Through simulations, we evaluate the performance of SBW 3.0 and analyze the effects of various critical parameters on information contribution. Our results validate the theoretical analysis, showing that the proposed consensus mechanism successfully encourages nodes and users to provide more information, thus overcoming the current limitations of Web 3.0 regarding data decentralization and management.

This paper presents PP-AICloud, a practical system prototype designed to enable privacy-preserving AI inference for cloud-based mobile services. Motivated by recent industry efforts, such as Apple's integration of Homomorphic Encryption (HE) for on-device intelligence, our work addresses the key limitations of existing privacy-preserving machine learning (PPML) solutions, i.e., high latency, bandwidth inefficiencies, and excessive on-device computation. PP-AICloud leverages edge nodes as an intermediate computing layer between mobile devices and centralized AICloud infrastructures, distributing the HE workflow across edge and cloud resources. By integrating HE with deep convolutional neural networks (CNNs), the system enables efficient and secure inference on encrypted user data without requiring decryption. Experimental results demonstrate that PP-AICloud achieves more than 91% accuracy on a real-world landmark recognition task with real-time latency of under 0.7 seconds. We demonstrate the capabilities of PP-AICloud through demo videos available at: Demo link.

This work proposes a novel privacy-preserving cyberattack detection framework for blockchain-based Internet of Things (IoT) systems. In our approach, artificial intelligence (AI)-driven detection modules are strategically deployed at blockchain nodes (BNs) to identify real-time attacks, ensuring high accuracy and minimal delay. To achieve this efficiency, the model training is conducted by a cloud service provider (CSP). Accordingly, BNs send their data to the CSP for training, but to safeguard privacy, the data is encrypted using homomorphic encryption (HE) before transmission. This encryption method allows the CSP to perform computations directly on encrypted data without the need for decryption, preserving data privacy throughout the learning process. To handle the substantial volume of encrypted data, we introduce an innovative packing algorithm in a single-instruction-multiple-data (SIMD) manner, enabling efficient training on HE-encrypted data. Building on this, we develop a novel deep neural network training algorithm optimized for encrypted data. We further propose a privacy-preserving distributed learning approach based on the FedAvg algorithm, which parallelizes the training across multiple workers, significantly improving computation time. Upon completion, the CSP distributes the trained model to the BNs, enabling them to perform real-time, privacy-preserved detection. Our simulation results demonstrate that our proposed method can not only mitigate the training time but also achieve detection accuracy that is approximately identical to the approach without encryption, with a gap of around 0.01%. Additionally, our real implementations on various blockchain consensus algorithms and hardware configurations show that our proposed framework can also be effectively adapted to real-world systems.

This paper aims to propose a novel framework to address the data privacy issue for Federated Learning (FL)-based Intrusion Detection Systems (IDSs) in Internet-of-Vehicles (IoVs) with limited computational resources. In particular, in conventional FL systems, it is usually assumed that the computing nodes have sufficient computational resources to process the training tasks. However, in practical IoV systems, vehicles usually have limited computational resources to process intensive training tasks, compromising the effectiveness of deploying FL in IDSs. While offloading data from vehicles to the cloud can mitigate this issue, it introduces significant privacy concerns for vehicle users (VUs). To resolve this issue, we first propose a highly-effective framework using homomorphic encryption to secure data that requires offloading to a centralized server for processing. Furthermore, we develop an effective training algorithm tailored to handle the challenges of FL-based systems with encrypted data. This algorithm allows the centralized server to directly compute on quantum-secure encrypted ciphertexts without needing decryption. This approach not only safeguards data privacy during the offloading process from VUs to the centralized server but also enhances the efficiency of utilizing FL for IDSs in IoV systems. Our simulation results show that our proposed approach can achieve a performance that is as close to that of the solution without encryption, with a gap of less than 0.8%.

Federated Learning (FL) offers a privacy-preserving approach to training machine learning models from distributed data on resource-constrained devices. However, even for modern/high-end cars vehicular networks, onboard training with the whole of the raw data presents challenges due to limited computing capability and power consumption concerns. To address this conundrum, we propose a novel FL framework that leverages homomorphic encyption (HE) to allow vehicles to upload encrypted portions of their data to a cloud server. Thanks to the key feature of HE, the server can perform additional model updates directly on the encrypted data, alleviating the workload on vehicles while preserving privacy. Furthermore, model updates from vehicles are also HE-encrypted, guaranteeing end-to-end privacy protection. This approach reduces the computational burden on vehicles while maintaining the model quality and convergence performance of the FL framework. Additionally, it can mitigate biases stemming from heterogeneous data, resulting in more stable FL convergence. Extensive experiments demonstrate the effectiveness of our framework in reducing workload and improving learning stability in vehicular networks.