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Privacy Preservation in Distributed Systems: Algorithms and Applications (Signals and Communication Technology)
by Anqi Zhang Ping Zhao Guanglin ZhangThis book provides a discussion of privacy in the following three parts: Privacy Issues in Data Aggregation; Privacy Issues in Indoor Localization; and Privacy-Preserving Offloading in MEC. In Part 1, the book proposes LocMIA, which shifts from membership inference attacks against aggregated location data to a binary classification problem, synthesizing privacy preserving traces by enhancing the plausibility of synthetic traces with social networks. In Part 2, the book highlights Indoor Localization to propose a lightweight scheme that can protect both location privacy and data privacy of LS. In Part 3, it investigates the tradeoff between computation rate and privacy protection for task offloading a multi-user MEC system, and verifies that the proposed load balancing strategy improves the computing service capability of the MEC system. In summary, all the algorithms discussed in this book are of great significance in demonstrating the importance of privacy.
Privacy Preservation in IoT: A Comprehensive Survey and Use Cases (SpringerBriefs in Computer Science)
by Youyang Qu Longxiang Gao Shui Yu Yong XiangThis 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.
Privacy Preservation of Genomic and Medical Data
by Amit Kumar TyagiPRIVACY PRESERVATION of GENOMIC and MEDICAL DATA Discusses topics concerning the privacy preservation of genomic data in the digital era, including data security, data standards, and privacy laws so that researchers in biomedical informatics, computer privacy and ELSI can assess the latest advances in privacy-preserving techniques for the protection of human genomic data. Privacy Preservation of Genomic and Medical Data focuses on genomic data sources, analytical tools, and the importance of privacy preservation. Topics discussed include tensor flow and Bio-Weka, privacy laws, HIPAA, and other emerging technologies like Internet of Things, IoT-based cloud environments, cloud computing, edge computing, and blockchain technology for smart applications. The book starts with an introduction to genomes, genomics, genetics, transcriptomes, proteomes, and other basic concepts of modern molecular biology. DNA sequencing methodology, DNA-binding proteins, and other related terms concerning genomes and genetics, and the privacy issues are discussed in detail. The book also focuses on genomic data sources, analyzing tools, and the importance of privacy preservation. It concludes with future predictions for genomic and genomic privacy, emerging technologies, and applications. Audience Researchers in information technology, data mining, health informatics and health technologies, clinical informatics, bioinformatics, security and privacy in healthcare, as well as health policy developers in public and private health departments and public health.
Privacy-Preserving Deep Learning: A Comprehensive Survey (SpringerBriefs on Cyber Security Systems and Networks)
by Kwangjo Kim Harry Chandra TanuwidjajaThis book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning. Google and Microsoft announced a major investment in PPDL in early 2019. This was followed by Google’s infamous announcement of “Private Join and Compute,” an open source PPDL tools based on secure multi-party computation (secure MPC) and homomorphic encryption (HE) in June of that year. One of the challenging issues concerning PPDL is selecting its practical applicability despite the gap between the theory and practice. In order to solve this problem, it has recently been proposed that in addition to classical privacy-preserving methods (HE, secure MPC, differential privacy, secure enclaves), new federated or split learning for PPDL should also be applied. This concept involves building a cloud framework that enables collaborative learning while keeping training data on client devices. This successfully preserves privacy and while allowing the framework to be implemented in the real world. This book provides fundamental insights into privacy-preserving and deep learning, offering a comprehensive overview of the state-of-the-art in PPDL methods. It discusses practical issues, and leveraging federated or split-learning-based PPDL. Covering the fundamental theory of PPDL, the pros and cons of current PPDL methods, and addressing the gap between theory and practice in the most recent approaches, it is a valuable reference resource for a general audience, undergraduate and graduate students, as well as practitioners interested learning about PPDL from the scratch, and researchers wanting to explore PPDL for their applications.
Privacy-Preserving in Edge Computing (Wireless Networks)
by Longxiang Gao Tom H. Luan Bruce Gu Youyang Qu Yong XiangWith the rapid development of big data, it is necessary to transfer the massive data generated by end devices to the cloud under the traditional cloud computing model. However, the delays caused by massive data transmission no longer meet the requirements of various real-time mobile services. Therefore, the emergence of edge computing has been recently developed as a new computing paradigm that can collect and process data at the edge of the network, which brings significant convenience to solving problems such as delay, bandwidth, and off-loading in the traditional cloud computing paradigm. By extending the functions of the cloud to the edge of the network, edge computing provides effective data access control, computation, processing and storage for end devices. Furthermore, edge computing optimizes the seamless connection from the cloud to devices, which is considered the foundation for realizing the interconnection of everything. However, due to the open features of edge computing, such as content awareness, real-time computing and parallel processing, the existing problems of privacy in the edge computing environment have become more prominent. The access to multiple categories and large numbers of devices in edge computing also creates new privacy issues. In this book, we discuss on the research background and current research process of privacy protection in edge computing. In the first chapter, the state-of-the-art research of edge computing are reviewed. The second chapter discusses the data privacy issue and attack models in edge computing. Three categories of privacy preserving schemes will be further introduced in the following chapters. Chapter three introduces the context-aware privacy preserving scheme. Chapter four further introduces a location-aware differential privacy preserving scheme. Chapter five presents a new blockchain based decentralized privacy preserving in edge computing. Chapter six summarize this monograph and propose future research directions. In summary, this book introduces the following techniques in edge computing: 1) describe an MDP-based privacy-preserving model to solve context-aware data privacy in the hierarchical edge computing paradigm; 2) describe a SDN based clustering methods to solve the location-aware privacy problems in edge computing; 3) describe a novel blockchain based decentralized privacy-preserving scheme in edge computing. These techniques enable the rapid development of privacy-preserving in edge computing.
Privacy-Preserving in Mobile Crowdsensing
by Chuan Zhang Tong Wu Youqi Li Liehuang ZhuMobile crowdsensing is a new sensing paradigm that utilizes the intelligence of a crowd of individuals to collect data for mobile purposes by using their portable devices, such as smartphones and wearable devices. Commonly, individuals are incentivized to collect data to fulfill a crowdsensing task released by a data requester. This “sensing as a service” elaborates our knowledge of the physical world by opening up a new door of data collection and analysis. However, with the expansion of mobile crowdsensing, privacy issues urgently need to be solved. In this book, we discuss the research background and current research process of privacy protection in mobile crowdsensing. In the first chapter, the background, system model, and threat model of mobile crowdsensing are introduced. The second chapter discusses the current techniques to protect user privacy in mobile crowdsensing. Chapter three introduces the privacy-preserving content-based task allocation scheme. Chapter four further introduces the privacy-preserving location-based task scheme. Chapter five presents the scheme of privacy-preserving truth discovery with truth transparency. Chapter six proposes the scheme of privacy-preserving truth discovery with truth hiding. Chapter seven summarizes this monograph and proposes future research directions. In summary, this book introduces the following techniques in mobile crowdsensing: 1) describe a randomizable matrix-based task-matching method to protect task privacy and enable secure content-based task allocation; 2) describe a multi-clouds randomizable matrix-based task-matching method to protect location privacy and enable secure arbitrary range queries; and 3) describe privacy-preserving truth discovery methods to support efficient and secure truth discovery. These techniques are vital to the rapid development of privacy-preserving in mobile crowdsensing.
Privacy-Preserving Machine Learning
by J. Morris Chang Di Zhuang G. Dumindu SamaraweeraKeep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models.In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation Privacy-enhancing technologies for data mining and database applications Compressive privacy for machine learning Privacy-Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You&’ll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. By the time you&’re done reading, you&’ll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance. About the Technology Machine learning applications need massive amounts of data. It&’s up to you to keep the sensitive information in those data sets private and secure. Privacy preservation happens at every point in the ML process, from data collection and ingestion to model development and deployment. This practical book teaches you the skills you&’ll need to secure your data pipelines end to end. About the Book Privacy-Preserving Machine Learning explores privacy preservation techniques through real-world use cases in facial recognition, cloud data storage, and more. You&’ll learn about practical implementations you can deploy now, future privacy challenges, and how to adapt existing technologies to your needs. Your new skills build towards a complete security data platform project you&’ll develop in the final chapter. What&’s Inside Differential and compressive privacy techniques Privacy for frequency or mean estimation, naive Bayes classifier, and deep learning Privacy-preserving synthetic data generation Enhanced privacy for data mining and database applications About the Reader For machine learning engineers and developers. Examples in Python and Java. About the Author J. Morris Chang is a professor at the University of South Florida. His research projects have been funded by DARPA and the DoD. Di Zhuang is a security engineer at Snap Inc. Dumindu Samaraweera is an assistant research professor at the University of South Florida. The technical editor for this book, Wilko Henecka, is a senior software engineer at Ambiata where he builds privacy-preserving software. Table of Contents PART 1 - BASICS OF PRIVACY-PRESERVING MACHINE LEARNING WITH DIFFERENTIAL PRIVACY 1 Privacy considerations in machine learning 2 Differential privacy for machine learning 3 Advanced concepts of differential privacy for machine learning PART 2 - LOCAL DIFFERENTIAL PRIVACY AND SYNTHETIC DATA GENERATION 4 Local differential privacy for machine learning 5 Advanced LDP mechanisms for machine learning 6 Privacy-preserving synthetic data generation PART 3 - BUILDING PRIVACY-ASSURED MACHINE LEARNING APPLICATIONS 7 Privacy-preserving data mining techniques 8 Privacy-preserving data management and operations 9 Compressive privacy for machine learning 10 Putting it all together: Designing a privacy-enhanced platform (DataHub)
Privacy-Preserving Machine Learning (SpringerBriefs on Cyber Security Systems and Networks)
by Jin Li Ping Li Zheli Liu Xiaofeng Chen Tong LiThis 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.
Privacy-Preserving Techniques with e-Healthcare Applications (Wireless Networks)
by Dan Zhu Dengguo Feng Xuemin (Sherman) ShenThis book investigates novel accurate and efficient privacy-preserving techniques and their applications in e-Healthcare services. The authors first provide an overview and a general architecture of e-Healthcare and delve into discussions on various applications within the e-Healthcare domain. Simultaneously, they analyze the privacy challenges in e-Healthcare services. Then, in Chapter 2, the authors give a comprehensive review of privacy-preserving and machine learning techniques applied in their proposed solutions. Specifically, Chapter 3 presents an efficient and privacy-preserving similar patient query scheme over high-dimensional and non-aligned genomic data; Chapter 4 and Chapter 5 respectively propose an accurate and privacy-preserving similar image retrieval scheme and medical pre-diagnosis scheme over dimension-related medical images and single-label medical records; Chapter 6 presents an efficient and privacy-preserving multi-disease simultaneous diagnosis scheme over medical records with multiple labels. Finally, the authors conclude the monograph and discuss future research directions of privacy-preserving e-Healthcare services in Chapter 7.
Privacy, Regulations, and Cybersecurity: The Essential Business Guide
by Chris MoschovitisProtect business value, stay compliant with global regulations, and meet stakeholder demands with this privacy how-to Privacy, Regulations, and Cybersecurity: The Essential Business Guide is your guide to understanding what “privacy” really means in a corporate environment: how privacy is different from cybersecurity, why privacy is essential for your business, and how to build privacy protections into your overall cybersecurity plan. First, author Chris Moschovitis walks you through our evolving definitions of privacy, from the ancient world all the way to the General Law on Data Protection (GDPR). He then explains—in friendly, accessible language—how to orient your preexisting cybersecurity program toward privacy, and how to make sure your systems are compliant with current regulations. This book—a sequel to Moschovitis’ well-received Cybersecurity Program Development for Business—explains which regulations apply in which regions, how they relate to the end goal of privacy, and how to build privacy into both new and existing cybersecurity programs. Keeping up with swiftly changing technology and business landscapes is no easy task. Moschovitis provides down-to-earth, actionable advice on how to avoid dangerous privacy leaks and protect your valuable data assets. Learn how to design your cybersecurity program with privacy in mind Apply lessons from the GDPR and other landmark laws Remain compliant and even get ahead of the curve, as privacy grows from a buzzword to a business must Learn how to protect what’s of value to your company and your stakeholders, regardless of business size or industry Understand privacy regulations from a business standpoint, including which regulations apply and what they require Think through what privacy protections will mean in the post-COVID environment Whether you’re new to cybersecurity or already have the fundamentals, this book will help you design and build a privacy-centric, regulation-compliant cybersecurity program.
Privacy, Security And Forensics in The Internet of Things (IoT)
by Ian Mitchell Reza Montasari Fiona Carroll Sukhvinder Hara Rachel Bolton-KingThis book provides the most recent security, privacy, technical and legal challenges in the IoT environments. This book offers a wide range of theoretical and technical solutions to address these challenges. Topics covered in this book include; IoT, privacy, ethics and security, the use of machine learning algorithms in classifying malicious websites, investigation of cases involving cryptocurrency, the challenges police and law enforcement face in policing cyberspace, the use of the IoT in modern terrorism and violent extremism, the challenges of the IoT in view of industrial control systems, and the impact of social media platforms on radicalisation to terrorism and violent extremism.This book also focuses on the ethical design of the IoT and the large volumes of data being collected and processed in an attempt to understand individuals’ perceptions of data and trust. A particular emphasis is placed on data ownership and perceived rights online. It examines cyber security challenges associated with the IoT, by making use of Industrial Control Systems, using an example with practical real-time considerations. Furthermore, this book compares and analyses different machine learning techniques, i.e., Gaussian Process Classification, Decision Tree Classification, and Support Vector Classification, based on their ability to learn and detect the attributes of malicious web applications. The data is subjected to multiple steps of pre-processing including; data formatting, missing value replacement, scaling and principal component analysis. This book has a multidisciplinary approach. Researchers working within security, privacy, technical and legal challenges in the IoT environments and advanced-level students majoring in computer science will find this book useful as a reference. Professionals working within this related field will also want to purchase this book.
Privacy Symposium 2022: Data Protection Law International Convergence and Compliance with Innovative Technologies (DPLICIT)
by Stefan Schiffner Sebastien Ziegler Adrian Quesada RodriguezThis book presents a collection of high-quality research works and professional perspectives arising from the call for papers of the Privacy Symposium 2022; an international conference aimed towards the promotion of international dialogue, cooperation and knowledge sharing on data protection regulations, compliance and emerging technologies. Gathering legal and technology expertise, this publication provides cutting-edge perspectives on the convergence of international data protection regulations, as well as data protection compliance of emerging technologies, such as: Artificial Intelligence, e-health, blockchain, edge computing, Internet of things, V2X and Smart Grids. It includes selected papers from the Privacy Symposium conference 2022 (PSC 2022) call for papers, encompassing relevant topics, including: international law and comparative law in data protection cross-border data transfers emerging technologies and data protection compliance data protection by design technologic solutions for data protection compliance data protection good practices across industries and verticals cybersecurity and data protection assessment and certification of data protection compliance, and data subject rights implementation This publication includes papers authored by academics and professionals involved on various areas of data protection, technical, legal and compliance services.Chapter 10 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com
Privacy Symposium 2023: Data Protection Law International Convergence and Compliance with Innovative Technologies (DPLICIT)
by Stefan Schiffner Sébastien Ziegler Meiko JensenThis book presents the proceedings of the Privacy Symposium 2023. the book features a collection of high-quality research works and professional perspectives on personal data protection and emerging technologies. Gathering legal and technology expertise, it provides cutting-edge perspective on international data protection regulations convergence, as well as data protection compliance of emerging technologies, such as artificial intelligence, e-health, blockchain, edge computing, Internet of Things, V2X and smart grid. Papers encompass various topics, including international law and comparative law in data protection and compliance, cross-border data transfer, emerging technologies and data protection compliance, data protection by design, technology for compliance and data protection, data protection good practices across industries and verticals, cybersecurity and data protection, assessment and certification of data protection compliance, and data subject rights implementation.
Privacy Technologies and Policy: 8th Annual Privacy Forum, APF 2020, Lisbon, Portugal, October 22–23, 2020, Proceedings (Lecture Notes in Computer Science #12121)
by Luís Antunes Maurizio Naldi Giuseppe F. Italiano Kai Rannenberg Prokopios DrogkarisThis book constitutes the refereed conference proceedings of the 8th Annual Privacy Forum, APF 2020, held in Lisbon, Portugal, in October 2020.The 12 revised full papers were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on impact assessment; privacy by design; data protection and security; and transparency.
Privacy Technologies and Policy
by Bettina Berendt Thomas Engel Demosthenes Ikonomou Daniel Le Métayer Stefan SchiffnerThis book constitutes the thoroughly refereedpost-conference proceedings of the Third Annual Privacy Forum, APF 2015, heldin Luxembourg, Luxembourg, in October 2015. The 11 revised full papers presented in this volume werecarefully reviewed and selected from 24 submissions. The topics focus onprivacy by design (PbD), i. e. the attempt to combine technical and organizationalmeasures to ensure the basic rights of the individual. The papers are organizedin three sessions: measuring privacy; rules and principles; legal and economicperspectives on privacy.
Privacy Technologies and Policy: 9th Annual Privacy Forum, APF 2021, Oslo, Norway, June 17–18, 2021, Proceedings (Lecture Notes in Computer Science #12703)
by Nils Gruschka Luís Filipe Coelho Antunes Kai Rannenberg Prokopios DrogkarisThis book constitutes the refereed conference proceedings of the 9th Annual Privacy Forum, APF 2021. Due to COVID-19 pandemic the conference was held virtually. The 9 revised full papers were carefully reviewed and selected from 43 submissions. The papers are organized in topical sections on Implementing Personal Data Processing Principles; Privacy Enhancing Technologies; Promoting Compliance with the GDPR.
Privacy Technologies and Policy: 10th Annual Privacy Forum, APF 2022, Warsaw, Poland, June 23–24, 2022, Proceedings (Lecture Notes in Computer Science #13279)
by Agnieszka Gryszczyńska Przemysław Polański Nils Gruschka Kai Rannenberg Monika AdamczykThis book constitutes the refereed conference proceedings of the 10th Annual Privacy Forum, APF 2022 in Warsaw, Poland in June 2022. The 8 full papers were carefully reviewed and selected from 38 submissions. The papers are organized in the area of privacy and data protection while focusing on privacy related application areas. A large focus of the 2022 conference was on the General Data Protection Regulation (GDPR).
Privacy Technologies and Policy: 5th Annual Privacy Forum, APF 2017, Vienna, Austria, June 7-8, 2017. Revised Selected Papers (Lecture Notes in Computer Science #10518)
by Manel Medina Andreas Mitrakas Kai Rannenberg Erich Schweighofer Nikolaos TsouroulasThis book constitutes the thoroughly refereed post-conference proceedings of the 6th Annual Privacy Forum, APF 2018, held in Barcelona, Spain, in June 2018. <P><P>The 11 revised full papers were carefully reviewed and selected from 49 submissions. The papers are grouped in topical sections named: technical analysis and techniques; privacy implementation; compliance; and legal aspects.
Privacy Technologies and Policy: 7th Annual Privacy Forum, APF 2019, Rome, Italy, June 13–14, 2019, Proceedings (Lecture Notes in Computer Science #11498)
by Maurizio Naldi Giuseppe F. Italiano Kai Rannenberg Manel Medina Athena BourkaThis book constitutes the refereed conference proceedings of the 7th Annual Privacy Forum, APF 2019, held in Rome,Italy, in June 2019. The 11 revised full papers were carefully reviewed and selected from 49 submissions. The papers present original work on the themes of data protection and privacy and their repercussions on technology, business, government, law, society, policy and law enforcement bridging the gap between research, business models, and policy. They are organized in topical sections on transparency, users' rights, risk assessment, and applications.
Privacy Technologies and Policy: 11th Annual Privacy Forum, APF 2023, Lyon, France, June 1–2, 2023, Proceedings (Lecture Notes in Computer Science #13888)
by Kai Rannenberg Prokopios Drogkaris Cédric LauradouxThis book constitutes the refereed conference proceedings of the 11th Annual Privacy Forum, APF 2023 in Lyon, France in June 2023. The 8 full papers were carefully reviewed and selected from 37 submissions. The papers are organized in the following topical sections: Emerging Technologies and Protection of Personal Data, Data Protection Principles and Data Subject Rights, Modelling Data Protection and Privacy, and Modelling Perceptions of Privacy.
Privacy Technologies and Policy: 12th Annual Privacy Forum, APF 2024, Karlstad, Sweden, September 4–5, 2024, Proceedings (Lecture Notes in Computer Science #14831)
by Kai Rannenberg Meiko Jensen Cédric LauradouxThis book constitutes the refereed proceedings of the 12th Annual Privacy Forum on Privacy Technologies and Policy, APF 2024, held in Karlstad, Sweden, during September 4–5, 2024. The 12 full papers were carefully reviewed and selected from 60 submissions. This conference was established as an opportunity to bring together key communities, namely policy, academia, and industry, in the broader area of privacy and data protection while focusing on privacy-related application areas. Like in the previous edition, a large focus of the 2024 conference was on the General Data Protection Regulation (GDPR) and the emerging legislation around the European Data Spaces and Arti cial Intelligence. Chapter 3, 9, 12 are licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). For further details see license information in the chapter.
Privacy Technologies and Policy
by Stefan Schiffner Jetzabel Serna Demosthenes Ikonomou Kai RannenbergThis book constitutes the refereed proceedings of the Second Annual Privacy Forum, APF 2014, held in Athens, Greece, in May 2014. The 12 revised papers presented in this volume were carefully reviewed and selected from 21 submissions. The topics include: the concept and implementation of "privacy by design", with applications to encrypted databases; the study of video surveillance architectures and new networking concepts and innovative solutions for identity management. The papers address the technical, legal, and economic aspects of these problems.
Privacy Technologies and Policy
by Erich Schweighofer Herbert Leitold Andreas Mitrakas Kai RannenbergThis book constitutes the thoroughly refereed post-conference proceedings of the 5th Annual Privacy Forum, APF 2017, held in Vienna, Austria, in June 2017. The 12 revised full papers were carefully selected from 41 submissions on the basis of significance, novelty, and scientific quality. These selected papers are organized in three different chapters corresponding to the conference sessions. The first chapter, "Data Protection Regulation", discusses topics concerning big genetic data, a privacy-preserving European identity ecosystem, the right to be forgotten und the re-use of privacy risk analysis. The second chapter, "Neutralisation and Anonymization", discusses neutralisation of threat actors, privacy by design data exchange between CSIRTs, differential privacy and database anonymization. Finally, the third chapter, "Privacy Policies in Practice", discusses privacy by design, privacy scores, privacy data management in healthcare and trade-offs between privacy and utility.
Privacy, Technology, and the Criminal Process (New Advances in Crime and Social Harm)
by Andrew Roberts, Joe Purshouse, and Jason BoslandThis collection considers the implications for privacy of the utilisation of new technologies in the criminal process. In most modern liberal democratic states, privacy is considered a basic right. Many national constitutions, and almost all international human rights instruments, include some guarantee of privacy. Yet privacy interests appear to have had relatively little influence on criminal justice policy making. The threat that technology poses to these interests demands critical re-evaluation of current law, policy, and practice. This is provided by the contributions to this volume. They offer legal, criminological, philosophical and comparative perspectives. The book will be of interest to legal and criminological scholars and postgraduate students. Its interdisciplinary methodology and focus on the intersection between law and technology make it also relevant for philosophers, and those interested in science and technology studies.
Privacy vs. Security
by Sophie Stalla-Bourdillon Joshua Phillips Mark D. RyanSecuring privacy in the current environment is one of the great challenges of today's democracies. Privacy vs. Security explores the issues of privacy and security and their complicated interplay, from a legal and a technical point of view. Sophie Stalla-Bourdillon provides a thorough account of the legal underpinnings of the European approach to privacy and examines their implementation through privacy, data protection and data retention laws. Joshua Philips and Mark D. Ryan focus on the technological aspects of privacy, in particular, on today's attacks on privacy by the simple use of today's technology, like web services and e-payment technologies and by State-level surveillance activities.