Privacy and data protection Books

Book SynopsisThis book presents the data privacy protection which has been extensively applied in our current era of big data. However, research into big data privacy is still in its infancy. Given the fact that existing protection methods can result in low data utility and unbalanced trade-offs, personalized privacy protection has become a rapidly expanding research topic.In this book, the authors explore emerging threats and existing privacy protection methods, and discuss in detail both the advantages and disadvantages of personalized privacy protection. Traditional methods, such as differential privacy and cryptography, are discussed using a comparative and intersectional approach, and are contrasted with emerging methods like federated learning and generative adversarial nets.The advances discussed cover various applications, e.g. cyber-physical systems, social networks, and location-based services. Given its scope, the book is of interest to scientists, policy-makers, researchers, and postgraduates alike.Table of Contents· Chapter 1: Introductiono Privacy research landscape o Personalized privacy overview o Contribution of this book o Remainder of the book · Chapter 2: Current Methods of Privacy Protection o Cryptography based methods o Differential privacy methods o Anonymity-based methods o Clustering-base methods o Machine learning and AI methods · Chapter 3: Privacy Attacks o Attack classification o Rationale of the attacks o The comparison of attacks · Chapter 4: Personalize Privacy Defense o Personalized privacy in cyber-physical systems o Personalized privacy in social networks o Personalized privacy in smart city o Personalized privacy in location-based services o Personalized privacy on the rise · Chapter 5: Future Directions o Trade-off optimization o Decentralized privacy protection o Privacy-preserving federated learning o Federated generative adversarial nets · Chapter6: Summary and Outlook

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Book SynopsisThis book provides a thorough overview of the evolution of privacy-preserving machine learning schemes over the last ten years, after discussing the importance of privacy-preserving techniques. In response to the diversity of Internet services, data services based on machine learning are now available for various applications, including risk assessment and image recognition. In light of open access to datasets and not fully trusted environments, machine learning-based applications face enormous security and privacy risks. In turn, it presents studies conducted to address privacy issues and a series of proposed solutions for ensuring privacy protection in machine learning tasks involving multiple parties. In closing, the book reviews state-of-the-art privacy-preserving techniques and examines the security threats they face.Table of ContentsIntroduction.- Secure Cooperative Learning in Early Years.- Outsourced Computation for Learning.- Secure Distributed Learning.- Learning with Differential Privacy.- Applications - Privacy-Preserving Image Processing.- Threats in Open Environment.- Conclusion.

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Book SynopsisThis book aims to sort out the clear logic of the development of machine learning-driven privacy preservation in IoTs, including the advantages and disadvantages, as well as the future directions in this under-explored domain. In big data era, an increasingly massive volume of data is generated and transmitted in Internet of Things (IoTs), which poses great threats to privacy protection. Motivated by this, an emerging research topic, machine learning-driven privacy preservation, is fast booming to address various and diverse demands of IoTs. However, there is no existing literature discussion on this topic in a systematically manner. The issues of existing privacy protection methods (differential privacy, clustering, anonymity, etc.) for IoTs, such as low data utility, high communication overload, and unbalanced trade-off, are identified to the necessity of machine learning-driven privacy preservation. Besides, the leading and emerging attacks pose further threats to privacy protection in this scenario. To mitigate the negative impact, machine learning-driven privacy preservation methods for IoTs are discussed in detail on both the advantages and flaws, which is followed by potentially promising research directions. Readers may trace timely contributions on machine learning-driven privacy preservation in IoTs. The advances cover different applications, such as cyber-physical systems, fog computing, and location-based services. This book will be of interest to forthcoming scientists, policymakers, researchers, and postgraduates. Table of Contents· Chapter 1: Introduction o Privacy research landscape o Machine learning driven privacy preservation overview o Contribution of this monograph o Outline of the monograph · Chapter 2: Current Methods of Privacy Protection in IoTs o Cryptography based methods o Differential privacy methods o Anonymity-based methods o Clustering-based methods · Chapter 3: Decentralized Privacy Protection of IoTs using Blockchain-Enabled Federated Learning o Overview o System Modelling o Decentralized Privacy Protocols o Blockchain-enabled Federated Learning · Chapter 4: Personalized Privacy Protection of IoTs using GAN-Enhanced Differential Privacy o Overview o System Modelling o Personalized Privacy o GAN-Enhanced Differential Privacy · Chapter 5: Hybrid Privacy Protection of IoT using Reinforcement Learning o Overview o System Modelling o Hybrid Privacy o Markov Decision Process and Reinforcement Learning · Chapter 6: Future Directions o Trade-off optimization o Privacy preservation of digital twin o Privacy-preserving federated learning o Federated generative adversarial nets · Chapter 7: Summary and Outlook

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Book SynopsisThis book constitutes the thoroughly refereed post conference papers of the 4th International Conference on Blockchain and Trustworthy Systems, Blocksys 2022, held in Chengdu, China, in August 2022.The 26 full papers were carefully reviewed and selected from 56 submissions. The papers are organized in topical sections: Trustworthy Systems; Blockchain; Private Computing.Table of ContentsTrustworthy Systems.- Secure and Efficient Agreement Signing atop Blockchain and Decentralized Identity.- A Privacy-preserving Credit Bank Supervision Framework based on Redactable Blockchain.- A Trusted Storage System for Digital Object in the Human-cyber-physical Environment.- The Rolf of Absorptive Capacity in the Blockchain Enabled Traceability Alignment: an Empirical Investigation.- Data Attest: A Framework to Attest Off-Chain Data Authenticity.- Blockchain-based Healthcare and Medicine Data Sharing and Service System.- BCSChain: Blockchain-Based Ceramic Supply Chain.- Blockchain.- A Highly Scalable Blockchain-enabled DNS Architecture.- Traceable Ring Signature Schemes Based on SM2 Digital Signature Algorithm and Its Applications in the Evidence-Storage System.- Blockchain-enabled Techniques for Energy Internet of Things: A Review.- Blockchain-based Social Network Access Control Mechanism.- Latency Analysis for Raft Consensus on Hyperledger Fabric.- A Survey of Blockchain-based Stablecoin: Cryptocurrencies and Central Bank Digital Currencies.- Collusion Attack Analysis and Detection of DPoS Consensus Mechanism.- Decentralized Blockchain Transaction Scheme based on Digital Commitment.- Private Computing.- Research on Abnormal Transaction Detection Method for Blockchain.- Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction.- A Blockchain-based UAV-assisted Secure Forest Supervision and Data Sharing System.- Suspicious Customer Detection on the Blockchain Network for Cryptocurrency Exchanges.- Real-time Detection of Cryptocurrency Mining Behavior.- Traffic Correlation for Deanonymizing Cryptocurrency Wallet Through Tor.- FL-MFGM: A Privacy-preserving and High-accuracy Blockchain Reliability Prediction Model.- Control-flow-based Analysis of Wasm Smart Contracts.- Blockchain Policy Tool Selection in China's Blockchain Industry Clustering Areas.- Lock-based Proof of Authority: A Faster and Low-Forking PoA Fault Tolerance Protocol for Blockchain Systems.- Phishing Fraud Detection on Ethereum Using Graph Neural Network.

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Book SynopsisThe continuous evolution and widespread application of communication technology, network technology and computing technology have promoted the intelligent interconnection of all things and ubiquitous sharing of information. The cross-border, cross-system, and cross-ecosystem exchange of user data has become commonplace. At the same time, difficulties in the prevention of private information abuse and lack of protection methods have become global problems. As such, there is an urgent need to intensify basic theoretical research in this field to support the protection of personal information in a ubiquitously interconnected environment. The authors of this book proposed the concept, definition and research scope of privacy computing for the first time in 2015. This book represents their original and innovative scientific research achievement dedicated to privacy computing research, and systematically explains the basic theory and technology involved. It introduces readers to the connection between personal information and privacy protection, defines privacy protection and privacy desensitization, clarifies and summarizes the limitations of existing privacy-preserving technologies in practical information system applications, analyzes the necessity of conducting privacy computing research, and proposes the concept, definition and research scope of privacy computing. It comprehensively expounds the theoretical system of privacy computing and some privacy-preserving algorithms based on the idea of privacy computing. In closing, it outlines future research directions.Table of ContentsChapter 1 Introduction.- Chapter 2 Privacy Protection Related Technologies.- Chapter 3 Privacy Computing Theory.- Chapter 4 Privacy Computing Technology.- Chapter 5 The future trend of privacy computing.

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Book Synopsis

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