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This book investigates decentralized trust-based privacy-preserving solutions in smart cities. The authors first present an overview of smart cities and privacy challenges and discuss the benefits of adopting decentralized trust models in achieving privacy preservation. The authors then give a comprehensive review of fundamental decentralized techniques and privacy-preserving cryptographic techniques. The next four chapters each detail a decentralized trust-based scheme, focusing respectively on privacy-preserving identity management, cross-domain authentication, data analytics, and data search, in specific use cases. Finally, the book explores open issues and outlines future research directions in the field of decentralized privacy preservation. Discusses benefits and challenges of applying decentralized trusts in smart cities Provides scheme designs and security analysis for use cases in smart cities, offering insights into applications Includes cryptographic knowledge accompanied by practical algorithm and protocol implementations.

Cooperating objects (Computer systems). --- Telecommunication. --- Data protection --- Cyber-Physical Systems. --- Communications Engineering, Networks. --- Privacy. --- Law and legislation.

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This book introduces a new type of data poisoning attack, dubbed, backdoor attack. In backdoor attacks, an attacker can train the model with poisoned data to obtain a model that performs well on a normal input but behaves wrongly with crafted triggers. Backdoor attacks can occur in many scenarios where the training process is not entirely controlled, such as using third-party datasets, third-party platforms for training, or directly calling models provided by third parties. Due to the enormous threat that backdoor attacks pose to model supply chain security, they have received widespread attention from academia and industry. This book focuses on exploiting backdoor attacks in the three types of DNN applications, which are image classification, natural language processing, and federated learning. Based on the observation that DNN models are vulnerable to small perturbations, this book demonstrates that steganography and regularization can be adopted to enhance the invisibility of backdoor triggers. Based on image similarity measurement, this book presents two metrics to quantitatively measure the invisibility of backdoor triggers. The invisible trigger design scheme introduced in this book achieves a balance between the invisibility and the effectiveness of backdoor attacks. In the natural language processing domain, it is difficult to design and insert a general backdoor in a manner imperceptible to humans. Any corruption to the textual data (e.g., misspelled words or randomly inserted trigger words/sentences) must retain context-awareness and readability to human inspectors. This book introduces two novel hidden backdoor attacks, targeting three major natural language processing tasks, including toxic comment detection, neural machine translation, and question answering, depending on whether the targeted NLP platform accepts raw Unicode characters. The emerged distributed training framework, i.e., federated learning, has advantages in preserving users' privacy. It has been widely used in electronic medical applications, however, it also faced threats derived from backdoor attacks. This book presents a novel backdoor detection framework in FL-based e-Health systems. We hope this book can provide insightful lights on understanding the backdoor attacks in different types of learning-based algorithms, including computer vision, natural language processing, and federated learning. The systematic principle in this book also offers valuable guidance on the defense of backdoor attacks against future learning-based algorithms.

Computer networks. --- Wireless communication systems. --- Mobile communication systems. --- Machine learning. --- Computer Communication Networks. --- Wireless and Mobile Communication. --- Machine Learning.