Privacy Preserving Computing

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Algorithms for Data and Computation Privacy

Alex X. Liu,Rui Li
Algorithms for Data and Computation Privacy Book PDF

Author : Alex X. Liu,Rui Li
Publisher : Springer Nature
Page : 404 pages
File Size : 46,5 Mb
Release : 2020-11-28
Category : Computers
ISBN : 9783030588960

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Algorithms for Data and Computation Privacy by Alex X. Liu,Rui Li Pdf

This book introduces the state-of-the-art algorithms for data and computation privacy. It mainly focuses on searchable symmetric encryption algorithms and privacy preserving multi-party computation algorithms. This book also introduces algorithms for breaking privacy, and gives intuition on how to design algorithm to counter privacy attacks. Some well-designed differential privacy algorithms are also included in this book. Driven by lower cost, higher reliability, better performance, and faster deployment, data and computing services are increasingly outsourced to clouds. In this computing paradigm, one often has to store privacy sensitive data at parties, that cannot fully trust and perform privacy sensitive computation with parties that again cannot fully trust. For both scenarios, preserving data privacy and computation privacy is extremely important. After the Facebook–Cambridge Analytical data scandal and the implementation of the General Data Protection Regulation by European Union, users are becoming more privacy aware and more concerned with their privacy in this digital world. This book targets database engineers, cloud computing engineers and researchers working in this field. Advanced-level students studying computer science and electrical engineering will also find this book useful as a reference or secondary text.

Privacy-Preserving Data Mining

Charu C. Aggarwal,Philip S. Yu
Privacy Preserving Data Mining Book PDF

Author : Charu C. Aggarwal,Philip S. Yu
Publisher : Springer Science & Business Media
Page : 524 pages
File Size : 47,9 Mb
Release : 2008-06-10
Category : Computers
ISBN : 9780387709925

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Privacy-Preserving Data Mining by Charu C. Aggarwal,Philip S. Yu Pdf

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.

The Ethics of Cybersecurity

Markus Christen,Bert Gordijn,Michele Loi
The Ethics of Cybersecurity Book PDF

Author : Markus Christen,Bert Gordijn,Michele Loi
Publisher : Springer Nature
Page : 388 pages
File Size : 42,5 Mb
Release : 2020-02-10
Category : Philosophy
ISBN : 9783030290535

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The Ethics of Cybersecurity by Markus Christen,Bert Gordijn,Michele Loi Pdf

This open access book provides the first comprehensive collection of papers that provide an integrative view on cybersecurity. It discusses theories, problems and solutions on the relevant ethical issues involved. This work is sorely needed in a world where cybersecurity has become indispensable to protect trust and confidence in the digital infrastructure whilst respecting fundamental values like equality, fairness, freedom, or privacy. The book has a strong practical focus as it includes case studies outlining ethical issues in cybersecurity and presenting guidelines and other measures to tackle those issues. It is thus not only relevant for academics but also for practitioners in cybersecurity such as providers of security software, governmental CERTs or Chief Security Officers in companies.

Privacy Preserving Data Mining

Jaideep Vaidya,Christopher W. Clifton,Yu Michael Zhu
Privacy Preserving Data Mining Book PDF

Author : Jaideep Vaidya,Christopher W. Clifton,Yu Michael Zhu
Publisher : Springer Science & Business Media
Page : 124 pages
File Size : 49,6 Mb
Release : 2006-09-28
Category : Computers
ISBN : 9780387294896

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Privacy Preserving Data Mining by Jaideep Vaidya,Christopher W. Clifton,Yu Michael Zhu Pdf

Privacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in contributing the data. This volume provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. Crystallizing much of the underlying foundation, the book aims to inspire further research in this new and growing area. Privacy Preserving Data Mining is intended to be accessible to industry practitioners and policy makers, to help inform future decision making and legislation, and to serve as a useful technical reference.

Privacy-Preserving Deep Learning

Kwangjo Kim,Harry Chandra Tanuwidjaja
Privacy Preserving Deep Learning Book PDF

Author : Kwangjo Kim,Harry Chandra Tanuwidjaja
Publisher : Springer Nature
Page : 81 pages
File Size : 53,5 Mb
Release : 2021-07-22
Category : Computers
ISBN : 9789811637643

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Privacy-Preserving Deep Learning by Kwangjo Kim,Harry Chandra Tanuwidjaja Pdf

This 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.

Secure and Privacy-Preserving Data Communication in Internet of Things

Liehuang Zhu,Zijian Zhang,Chang Xu
Secure and Privacy Preserving Data Communication in Internet of Things Book PDF

Author : Liehuang Zhu,Zijian Zhang,Chang Xu
Publisher : Springer
Page : 78 pages
File Size : 46,8 Mb
Release : 2017-02-22
Category : Technology & Engineering
ISBN : 9789811032356

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Secure and Privacy-Preserving Data Communication in Internet of Things by Liehuang Zhu,Zijian Zhang,Chang Xu Pdf

This book mainly concentrates on protecting data security and privacy when participants communicate with each other in the Internet of Things (IoT). Technically, this book categorizes and introduces a collection of secure and privacy-preserving data communication schemes/protocols in three traditional scenarios of IoT: wireless sensor networks, smart grid and vehicular ad-hoc networks recently. This book presents three advantages which will appeal to readers. Firstly, it broadens reader’s horizon in IoT by touching on three interesting and complementary topics: data aggregation, privacy protection, and key agreement and management. Secondly, various cryptographic schemes/protocols used to protect data confidentiality and integrity is presented. Finally, this book will illustrate how to design practical systems to implement the algorithms in the context of IoT communication. In summary, readers can simply learn and directly apply the new technologies to communicate data in IoT after reading this book.

Privacy-preserving Computing

Kai Chen,Qiang Yang
Privacy preserving Computing Book PDF

Author : Kai Chen,Qiang Yang
Publisher : Cambridge University Press
Page : 270 pages
File Size : 54,7 Mb
Release : 2023-11-16
Category : Computers
ISBN : 9781009299503

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Privacy-preserving Computing by Kai Chen,Qiang Yang Pdf

Privacy-preserving computing aims to protect the personal information of users while capitalizing on the possibilities unlocked by big data. This practical introduction for students, researchers, and industry practitioners is the first cohesive and systematic presentation of the field's advances over four decades. The book shows how to use privacy-preserving computing in real-world problems in data analytics and AI, and includes applications in statistics, database queries, and machine learning. The book begins by introducing cryptographic techniques such as secret sharing, homomorphic encryption, and oblivious transfer, and then broadens its focus to more widely applicable techniques such as differential privacy, trusted execution environment, and federated learning. The book ends with privacy-preserving computing in practice in areas like finance, online advertising, and healthcare, and finally offers a vision for the future of the field.

Introduction to Privacy-Preserving Data Publishing

Benjamin C.M. Fung,Ke Wang,Ada Wai-Chee Fu,Philip S. Yu
Introduction to Privacy Preserving Data Publishing Book PDF

Author : Benjamin C.M. Fung,Ke Wang,Ada Wai-Chee Fu,Philip S. Yu
Publisher : CRC Press
Page : 374 pages
File Size : 44,6 Mb
Release : 2010-08-02
Category : Computers
ISBN : 9781420091502

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Introduction to Privacy-Preserving Data Publishing by Benjamin C.M. Fung,Ke Wang,Ada Wai-Chee Fu,Philip S. Yu Pdf

Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Int

Privacy-Preserving Machine Learning

Srinivasa Rao Aravilli
Privacy Preserving Machine Learning Book PDF

Author : Srinivasa Rao Aravilli
Publisher : Packt Publishing Ltd
Page : 402 pages
File Size : 43,5 Mb
Release : 2024-05-24
Category : Computers
ISBN : 9781800564220

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Privacy-Preserving Machine Learning by Srinivasa Rao Aravilli Pdf

Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and algorithms to protect sensitive data from privacy breaches Key Features Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches Develop and deploy privacy-preserving ML pipelines using open-source frameworks Gain insights into confidential computing and its role in countering memory-based data attacks Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionPrivacy regulations are evolving each year and compliance with privacy regulations is mandatory for every enterprise. Machine learning engineers are required to not only analyze large amounts of data to gain crucial insights, but also comply with privacy regulations to protect sensitive data. This may seem quite challenging considering the large volume of data involved and lack of in-depth expertise in privacy-preserving machine learning. This book delves into data privacy, machine learning privacy threats, and real-world cases of privacy-preserving machine learning, as well as open-source frameworks for implementation. You’ll be guided through developing anti-money laundering solutions via federated learning and differential privacy. Dedicated sections also address data in-memory attacks and strategies for safeguarding data and ML models. The book concludes by discussing the necessity of confidential computation, privacy-preserving machine learning benchmarks, and cutting-edge research. By the end of this machine learning book, you’ll be well-versed in privacy-preserving machine learning and know how to effectively protect data from threats and attacks in the real world.What you will learn Study data privacy, threats, and attacks across different machine learning phases Explore Uber and Apple cases for applying differential privacy and enhancing data security Discover IID and non-IID data sets as well as data categories Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks Understand secure multiparty computation with PSI for large data Get up to speed with confidential computation and find out how it helps data in memory attacks Who this book is for This book is for data scientists, machine learning engineers, and privacy engineers who have working knowledge of mathematics as well as basic knowledge in any one of the ML frameworks (TensorFlow, PyTorch, or scikit-learn).

Federal Statistics, Multiple Data Sources, and Privacy Protection

National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Panel on Improving Federal Statistics for Policy and Social Science Research Using Multiple Data Sources and State-of-the-Art Estimation Methods
Federal Statistics Multiple Data Sources and Privacy Protection Book PDF

Author : National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Panel on Improving Federal Statistics for Policy and Social Science Research Using Multiple Data Sources and State-of-the-Art Estimation Methods
Publisher : National Academies Press
Page : 195 pages
File Size : 46,9 Mb
Release : 2018-01-27
Category : Social Science
ISBN : 9780309465373

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Federal Statistics, Multiple Data Sources, and Privacy Protection by National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Panel on Improving Federal Statistics for Policy and Social Science Research Using Multiple Data Sources and State-of-the-Art Estimation Methods Pdf

The environment for obtaining information and providing statistical data for policy makers and the public has changed significantly in the past decade, raising questions about the fundamental survey paradigm that underlies federal statistics. New data sources provide opportunities to develop a new paradigm that can improve timeliness, geographic or subpopulation detail, and statistical efficiency. It also has the potential to reduce the costs of producing federal statistics. The panel's first report described federal statistical agencies' current paradigm, which relies heavily on sample surveys for producing national statistics, and challenges agencies are facing; the legal frameworks and mechanisms for protecting the privacy and confidentiality of statistical data and for providing researchers access to data, and challenges to those frameworks and mechanisms; and statistical agencies access to alternative sources of data. The panel recommended a new approach for federal statistical programs that would combine diverse data sources from government and private sector sources and the creation of a new entity that would provide the foundational elements needed for this new approach, including legal authority to access data and protect privacy. This second of the panel's two reports builds on the analysis, conclusions, and recommendations in the first one. This report assesses alternative methods for implementing a new approach that would combine diverse data sources from government and private sector sources, including describing statistical models for combining data from multiple sources; examining statistical and computer science approaches that foster privacy protections; evaluating frameworks for assessing the quality and utility of alternative data sources; and various models for implementing the recommended new entity. Together, the two reports offer ideas and recommendations to help federal statistical agencies examine and evaluate data from alternative sources and then combine them as appropriate to provide the country with more timely, actionable, and useful information for policy makers, businesses, and individuals.

Privacy-Preserving in Edge Computing

Longxiang Gao,Tom H. Luan,Bruce Gu,Youyang Qu,Yong Xiang
Privacy Preserving in Edge Computing Book PDF

Author : Longxiang Gao,Tom H. Luan,Bruce Gu,Youyang Qu,Yong Xiang
Publisher : Springer Nature
Page : 113 pages
File Size : 43,6 Mb
Release : 2021-06-01
Category : Computers
ISBN : 9789811621994

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Privacy-Preserving in Edge Computing by Longxiang Gao,Tom H. Luan,Bruce Gu,Youyang Qu,Yong Xiang Pdf

With 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.

Security and Privacy Preserving for IoT and 5G Networks

Ahmed A. Abd El-Latif,Bassem Abd-El-Atty,Salvador E. Venegas-Andraca,Wojciech Mazurczyk,Brij B. Gupta
Security and Privacy Preserving for IoT and 5G Networks Book PDF

Author : Ahmed A. Abd El-Latif,Bassem Abd-El-Atty,Salvador E. Venegas-Andraca,Wojciech Mazurczyk,Brij B. Gupta
Publisher : Springer Nature
Page : 283 pages
File Size : 49,6 Mb
Release : 2021-10-09
Category : Computers
ISBN : 9783030854287

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Security and Privacy Preserving for IoT and 5G Networks by Ahmed A. Abd El-Latif,Bassem Abd-El-Atty,Salvador E. Venegas-Andraca,Wojciech Mazurczyk,Brij B. Gupta Pdf

This book presents state-of-the-art research on security and privacy- preserving for IoT and 5G networks and applications. The accepted book chapters covered many themes, including traceability and tamper detection in IoT enabled waste management networks, secure Healthcare IoT Systems, data transfer accomplished by trustworthy nodes in cognitive radio, DDoS Attack Detection in Vehicular Ad-hoc Network (VANET) for 5G Networks, Mobile Edge-Cloud Computing, biometric authentication systems for IoT applications, and many other applications It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this particular area or those interested in grasping its diverse facets and exploring the latest advances on security and privacy- preserving for IoT and 5G networks.

Privacy-Preserving Machine Learning

J. Morris Chang,Di Zhuang,G. Dumindu Samaraweera
Privacy Preserving Machine Learning Book PDF

Author : J. Morris Chang,Di Zhuang,G. Dumindu Samaraweera
Publisher : Simon and Schuster
Page : 334 pages
File Size : 51,9 Mb
Release : 2023-05-02
Category : Computers
ISBN : 9781617298042

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Privacy-Preserving Machine Learning by J. Morris Chang,Di Zhuang,G. Dumindu Samaraweera Pdf

Keep 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. 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)

Cryptography for Security and Privacy in Cloud Computing

Stefan Rass ,Daniel Slamanig
Cryptography for Security and Privacy in Cloud Computing Book PDF

Author : Stefan Rass ,Daniel Slamanig
Publisher : Artech House
Page : 264 pages
File Size : 51,8 Mb
Release : 2013-11-01
Category : Computers
ISBN : 9781608075751

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Cryptography for Security and Privacy in Cloud Computing by Stefan Rass ,Daniel Slamanig Pdf

As is common practice in research, many new cryptographic techniques have been developed to tackle either a theoretical question or foreseeing a soon to become reality application. Cloud computing is one of these new areas, where cryptography is expected to unveil its power by bringing striking new features to the cloud. Cloud computing is an evolving paradigm, whose basic attempt is to shift computing and storage capabilities to external service providers. This resource offers an overview of the possibilities of cryptography for protecting data and identity information, much beyond well-known cryptographic primitives such as encryption or digital signatures. This book represents a compilation of various recent cryptographic primitives, providing readers with the features and limitations of each.

Fog/Edge Computing For Security, Privacy, and Applications

Wei Chang,Jie Wu
Fog Edge Computing For Security Privacy and Applications Book PDF

Author : Wei Chang,Jie Wu
Publisher : Springer Nature
Page : 417 pages
File Size : 55,5 Mb
Release : 2021-01-04
Category : Computers
ISBN : 9783030573287

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Fog/Edge Computing For Security, Privacy, and Applications by Wei Chang,Jie Wu Pdf

This book provides the state-of-the-art development on security and privacy for fog/edge computing, together with their system architectural support and applications. This book is organized into five parts with a total of 15 chapters. Each area corresponds to an important snapshot. The first part of this book presents an overview of fog/edge computing, focusing on its relationship with cloud technology and the future with the use of 5G communication. Several applications of edge computing are discussed. The second part of this book considers several security issues in fog/edge computing, including the secure storage and search services, collaborative intrusion detection method on IoT-fog computing, and the feasibility of deploying Byzantine agreement protocols in untrusted environments. The third part of this book studies the privacy issues in fog/edge computing. It first investigates the unique privacy challenges in fog/edge computing, and then discusses a privacy-preserving framework for the edge-based video analysis, a popular machine learning application on fog/edge. This book also covers the security architectural design of fog/edge computing, including a comprehensive overview of vulnerabilities in fog/edge computing within multiple architectural levels, the security and intelligent management, the implementation of network-function-virtualization-enabled multicasting in part four. It explains how to use the blockchain to realize security services. The last part of this book surveys applications of fog/edge computing, including the fog/edge computing in Industrial IoT, edge-based augmented reality, data streaming in fog/edge computing, and the blockchain-based application for edge-IoT. This book is designed for academics, researchers and government officials, working in the field of fog/edge computing and cloud computing. Practitioners, and business organizations (e.g., executives, system designers, and marketing professionals), who conduct teaching, research, decision making, and designing fog/edge technology will also benefit from this book The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, and information systems, but also applies to students in business, education, and economics, who would benefit from the information, models, and case studies therein.