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Author: Guanglin Zhang
Publisher: Springer Nature
ISBN: 3031580133
Category :
Languages : en
Pages : 266
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Book Description
Author: Guanglin Zhang
Publisher: Springer Nature
ISBN: 3031580133
Category :
Languages : en
Pages : 266
Get Book Here
Book Description
Author: Jaideep Vaidya
Publisher: Springer Science & Business Media
ISBN: 0387294899
Category : Computers
Languages : en
Pages : 124
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Book Description
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.
Author: J. Morris Chang
Publisher: Simon and Schuster
ISBN: 1617298042
Category : Computers
Languages : en
Pages : 334
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Book Description
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)
Author: Markus Christen
Publisher: Springer Nature
ISBN: 3030290530
Category : Philosophy
Languages : en
Pages : 388
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Book Description
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.
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799853403
Category : Computers
Languages : en
Pages : 2700
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Book Description
Distributed systems intertwine with our everyday lives. The benefits and current shortcomings of the underpinning technologies are experienced by a wide range of people and their smart devices. With the rise of large-scale IoT and similar distributed systems, cloud bursting technologies, and partial outsourcing solutions, private entities are encouraged to increase their efficiency and offer unparalleled availability and reliability to their users. The Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing is a vital reference source that provides valuable insight into current and emergent research occurring within the field of distributed computing. It also presents architectures and service frameworks to achieve highly integrated distributed systems and solutions to integration and efficient management challenges faced by current and future distributed systems. Highlighting a range of topics such as data sharing, wireless sensor networks, and scalability, this multi-volume book is ideally designed for system administrators, integrators, designers, developers, researchers, academicians, and students.
Author: Chhabi Rani Panigrahi
Publisher: Springer
ISBN: 9789811565830
Category : Technology & Engineering
Languages : en
Pages : 439
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Book Description
This book features high-quality research papers presented at the 4th International Conference on Advanced Computing and Intelligent Engineering (ICACIE 2019), Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India. It includes sections describing technical advances and contemporary research in the fields of advanced computing and intelligent engineering, which are based on the presented articles. Intended for postgraduate students and researchers working in the discipline of computer science and engineering, the book also appeals to researchers in the domain of electronics as it covers hardware technologies and future communication technologies.
Author: Abdelkader Hameurlain
Publisher: Springer Nature
ISBN: 3662605317
Category : Computers
Languages : en
Pages : 143
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Book Description
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the 42nd issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, consists of five revised selected regular papers, presenting the following topics: Privacy-Preserving Top-k Query Processing in Distributed Systems; Trust Factors and Insider Threats in Permissioned Distributed Ledgers: An Analytical Study and Evaluation of Popular DLT Frameworks; Polystore and Tensor Data Model for Logical Data Independence and Impedance Mismatch in Big Data Analytics; A General Framework for Multiple Choice Question Answering Based on Mutual Information and Reinforced Co-occurrence; Rejig: A Scalable Online Algorithm for Cache Server Configuration Changes.
Author: Benjamin C.M. Fung
Publisher: CRC Press
ISBN: 1420091506
Category : Computers
Languages : en
Pages : 374
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Book Description
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
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799889556
Category : Computers
Languages : en
Pages : 2188
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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: Feng Bao
Publisher: Springer
ISBN: 3642312845
Category : Computers
Languages : en
Pages : 592
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Book Description
This book constitutes the refereed proceedings of the 10th International Conference on Applied Cryptography and Network Security, ACNS 2012, held in Singapore, in June 2012. The 33 revised full papers included in this volume were carefully reviewed and selected from 192 submissions. They are organized in topical sessions on authentication, key management, block ciphers, identity-based cryptography, cryptographic primitives, cryptanalysis, side channel attacks, network security, Web security, security and privacy in social networks, security and privacy in RFID systems, security and privacy in cloud systems, and security and privacy in smart grids.
