Personalized Privacy Protection in Big Data PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Personalized Privacy Protection in Big Data PDF full book. Access full book title Personalized Privacy Protection in Big Data by Youyang Qu. Download full books in PDF and EPUB format.
Author: Youyang Qu
Publisher: Springer Nature
ISBN: 9811637504
Category : Computers
Languages : en
Pages : 148
Get Book Here
Book Description
This 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.
Author: Youyang Qu
Publisher: Springer Nature
ISBN: 9811637504
Category : Computers
Languages : en
Pages : 148
Get Book Here
Book Description
This 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.
Author: Youyang Qu
Publisher:
ISBN: 9788981163754
Category : Big data
Languages : en
Pages : 0
Get Book Here
Book Description
This 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.
Author: Theresa Payton
Publisher: Rowman & Littlefield
ISBN: 1442225467
Category : Computers
Languages : en
Pages : 277
Get Book Here
Book Description
Digital devices have made our busy lives a little easier and they do great things for us, too – we get just-in-time coupons, directions, and connection with loved ones while stuck on an airplane runway. Yet, these devices, though we love them, can invade our privacy in ways we are not even aware of. The digital devices send and collect data about us whenever we use them, but that data is not always safeguarded the way we assume it should be to protect our privacy. Privacy is complex and personal. Many of us do not know the full extent to which data is collected, stored, aggregated, and used. As recent revelations indicate, we are subject to a level of data collection and surveillance never before imaginable. While some of these methods may, in fact, protect us and provide us with information and services we deem to be helpful and desired, others can turn out to be insidious and over-arching. Privacy in the Age of Big Data highlights the many positive outcomes of digital surveillance and data collection while also outlining those forms of data collection to which we do not always consent, and of which we are likely unaware, as well as the dangers inherent in such surveillance and tracking. Payton and Claypoole skillfully introduce readers to the many ways we are “watched” and how to change behaviors and activities to recapture and regain more of our privacy. The authors suggest remedies from tools, to behavior changes, to speaking out to politicians to request their privacy back. Anyone who uses digital devices for any reason will want to read this book for its clear and no-nonsense approach to the world of big data and what it means for all of us.
Author: Terence Craig
Publisher: "O'Reilly Media, Inc."
ISBN: 1449305008
Category : Computers
Languages : en
Pages : 95
Get Book Here
Book Description
"The players, regulators, and stakeholders"--Cover.
Author: Youyang Qu
Publisher: Springer Nature
ISBN: 9811917973
Category : Computers
Languages : en
Pages : 127
Get Book Here
Book Description
This 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.
Author: Yan Yan
Publisher: CRC Press
ISBN: 1040226159
Category : Computers
Languages : en
Pages : 137
Get Book Here
Book Description
This book examines the uses and potential risks of location-based services (LBS) in the context of big data, with a focus on location privacy protection methods. The growth of the mobile Internet and the popularity of smart devices have spurred the development of LBS and related mobile applications. However, the misuse of sensitive location data could compromise the physical and communication security of associated devices and nodes, potentially leading to privacy breaches. This book explores the potential risks to the location privacy of mobile users in the context of big data applications. It discusses the latest methods and implications of location privacy from different perspectives. The author offers case studies of three applications: statistical disclosure and privacy protection of location-based big data using a centralized differential privacy model; a user location perturbation mechanism based on a localized differential privacy model; and terminal location perturbation using a geo-indistinguishability model. Linking recent developments in three-dimensional positioning and artificial intelligence, the book also predicts future trends and provides insights into research issues in location privacy. This title will be a valuable resource for researchers, students, and professionals interested in location-based services, privacy computing and protection, wireless network security, and big data security.
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799889556
Category : Computers
Languages : en
Pages : 2188
Get Book Here
Book Description
With the immense amount of data that is now available online, security concerns have been an issue from the start, and have grown as new technologies are increasingly integrated in data collection, storage, and transmission. Online cyber threats, cyber terrorism, hacking, and other cybercrimes have begun to take advantage of this information that can be easily accessed if not properly handled. New privacy and security measures have been developed to address this cause for concern and have become an essential area of research within the past few years and into the foreseeable future. The ways in which data is secured and privatized should be discussed in terms of the technologies being used, the methods and models for security that have been developed, and the ways in which risks can be detected, analyzed, and mitigated. The Research Anthology on Privatizing and Securing Data reveals the latest tools and technologies for privatizing and securing data across different technologies and industries. It takes a deeper dive into both risk detection and mitigation, including an analysis of cybercrimes and cyber threats, along with a sharper focus on the technologies and methods being actively implemented and utilized to secure data online. Highlighted topics include information governance and privacy, cybersecurity, data protection, challenges in big data, security threats, and more. This book is essential for data analysts, cybersecurity professionals, data scientists, security analysts, IT specialists, practitioners, researchers, academicians, and students interested in the latest trends and technologies for privatizing and securing data.
Author: Xiaofeng Chen
Publisher: Springer Nature
ISBN: 9819785464
Category :
Languages : en
Pages : 281
Get Book Here
Book Description
Author: Yuan Tian
Publisher: Springer Nature
ISBN: 9811908524
Category : Computers
Languages : en
Pages : 761
Get Book Here
Book Description
This book constitutes the refereed proceedings of the Third International Conference on Big Data and Security, ICBDS 2021, held in Shenzhen, China, in November 2021 The 46 revised full papers and 13 short papers were carefully reviewed and selected out of 221 submissions. The papers included in this volume are organized according to the topical sections on cybersecurity and privacy; big data; blockchain and internet of things, and artificial intelligence/ machine learning security.
Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
ISBN: 0387709924
Category : Computers
Languages : en
Pages : 524
Get Book Here
Book Description
Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions of a particular topic in privacy. The book is designed for researchers, professors, and advanced-level students in computer science, but is also suitable for practitioners in industry.
