Download or read book From Data Protection to Knowledge Machines The Study of Law and Informatics written by Peter Seipel and published by Springer. This book was released on 1990-07-26 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book From Data Protection to Knowledge Machines The Study of Law and Informatics written by Peter Seipel and published by Springer. This book was released on 1990-07-26 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Protection and Privacy written by Computers, Privacy and Data Protection (Conference) and published by . This book was released on 2017 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: EU data protection and "treaty-base games" : when fundamental rights are wearing market-making clothes / Laima Janciute -- The "risk revolution" in EU data protection law : we can't have our cake and eat it, too / Claudia Quelle -- No privacy without transparency / Roger Taylor -- Machine learning with personal data / Dimitra Kamarinou, Christopher Millard, and Jatinder Singh -- Bridging regulation and practice : a legal-technical analysis of the three types of data in the GDPR / Runshan Hu, Sophie Stalla-Bourdillon, Mu Yang, Valeria Schiavo, and Vladimiro Sassone -- Are we prepared for the 4th industrial revolution : data protection and data security challenges of industry 4.0 in the EU context / Carolin Moeller -- Reasonable expectations of data protection in telerehabilitation : a legal and anthropological perspective on intelligent orthoses / Martina Klausner and Sebastian Golla -- Considering the privacy design implications of conversation as platform / Ewa Luger and Gilad Rosner -- CPDP 2017 closing speech / Giovanni Butarelli

Download or read book Data Protection Implementation Guide written by Brendan Quinn and published by Kluwer Law International B.V.. This book was released on 2021-09-02 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: The complexities of implementing the General Data Protection Regulation (GDPR) continue to grow as it progresses through new and ever-changing technologies, business models, codes of conduct, and decisions of the supervisory authorities, and the courts. This eminently practical guide to implementing the GDPR – written in an original, problem-solving style by a highly experienced data protection expert with equal knowledge of both law and technology – provides a step-by-step project management approach to building a GDPR-compliant data protection system, assessing, and documenting the risks and then implementing these changes through processes at the operational level. With detailed attention to case law (Member State, ECJ, and ECHR), especially where affecting high-risk areas that have attracted scrutiny, the guidance proceeds systematically through such topics and issues as the following: required documentation, policies, and procedures; risk assessment tools and analysis frameworks; children’s data; employee and health data; international transfers post-Schrems II; data subject rights including the right of access; data retention and erasure; tracking and surveillance; and effects of technologies such as artificial intelligence, biometrics, and machine learning. With its practical examples derived from the author’s experience in building GDPR-compliant software, as well as its analysis of case law and enforcement priorities, this incomparable guide enables company data protection officers and compliance staff to advise on key issues with full awareness of the legal and reputational risks and how to mitigate them. It is also sure to be of immeasurable value to concerned regulators and policymakers at all government levels. “…it's going to be the go to resource for practitioners.” Tom Gilligan, Data Protection Consultant, September 2021 "I purchased this book recently and I’m very glad I did. It’s the textbook I have been waiting for. As someone relatively new to data protection, I was finding it very difficult to find books on the practical side of data protection. This book is very clearly laid out with practical examples and case law given for each topic, which is immensely helpful. I would recommend it to any data protection practitioners." Jennifer Breslin, LLM CIPP/E, AIPP Member

Download or read book Data Protection and Privacy Volume 10 written by Ronald Leenes and published by Bloomsbury Publishing. This book was released on 2017-12-28 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: The subjects of Privacy and Data Protection are more relevant than ever with the European General Data Protection Regulation (GDPR) becoming enforceable in May 2018. This volume brings together papers that offer conceptual analyses, highlight issues, propose solutions, and discuss practices regarding privacy and data protection. It is one of the results of the tenth annual International Conference on Computers, Privacy and Data Protection, CPDP 2017, held in Brussels in January 2017. The book explores Directive 95/46/EU and the GDPR moving from a market framing to a 'treaty-base games frame', the GDPR requirements regarding machine learning, the need for transparency in automated decision-making systems to warrant against wrong decisions and protect privacy, the riskrevolution in EU data protection law, data security challenges of Industry 4.0, (new) types of data introduced in the GDPR, privacy design implications of conversational agents, and reasonable expectations of data protection in Intelligent Orthoses. This interdisciplinary book was written while the implications of the General Data Protection Regulation 2016/679 were beginning to become clear. It discusses open issues, and daring and prospective approaches. It will serve as an insightful resource for readers with an interest in computers, privacy and data protection.

Download or read book The Elements of Big Data Value written by Edward Curry and published by Springer Nature. This book was released on 2021-08-01 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation.

Download or read book Knowledge of the Law in the Big Data Age written by G. Peruginelli and published by IOS Press. This book was released on 2019-07-23 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: The changes brought about by digital technology and the consequent explosion of information known as Big Data have brought opportunities and challenges in all areas of society, and the law is no exception. This book, Knowledge of the Law in the Big Data Age contains a selection of the papers presented at the conference ‘Law via the Internet 2018’, held in Florence, Italy, on 11-12 October 2018. This annual conference of the ‘Free Access to Law Movement’ (http://www.fatlm.org) hosted more than 60 international speakers from universities, government and research bodies as well as EU institutions. Topics covered range from free access to law and Big Data and data analytics in the legal domain, to policy issues concerning access, publishing and the dissemination of legal information, tools to support democratic participation and opportunities for digital democracy. The book is divided into 3 sections: Part I provides an introductory background, covering aspects such as the evolution of legal science and models for representing the law; Part II addresses the present and future of access to law and to various legal information sources; and Part III covers updates in projects, initiatives, and concrete achievements in the field. The book provides an overview of the practical implementation of legal information systems and the tools to manage this special kind of information, as well as some of the critical issues which must be faced, and will be of interest to all those working at the intersection of law and technology.

Download or read book Data Protection in a Post Pandemic Society written by Chaminda Hewage and published by Springer Nature. This book was released on 2023-07-11 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers the latest research results and predictions in data protection with a special focus on post-pandemic society. This book also includes various case studies and applications on data protection. It includes the Internet of Things (IoT), smart cities, federated learning, Metaverse, cryptography and cybersecurity. Data protection has burst onto the computer security scene due to the increased interest in securing personal data. Data protection is a key aspect of information security where personal and business data need to be protected from unauthorized access and modification. The stolen personal information has been used for many purposes such as ransom, bullying and identity theft. Due to the wider usage of the Internet and social media applications, people make themselves vulnerable by sharing personal data. This book discusses the challenges associated with personal data protection prior, during and post COVID-19 pandemic. Some of these challenges are caused by the technological advancements (e.g. Artificial Intelligence (AI)/Machine Learning (ML) and ChatGPT). In order to preserve the privacy of the data involved, there are novel techniques such as zero knowledge proof, fully homomorphic encryption, multi-party computations are being deployed. The tension between data privacy and data utility drive innovation in this area where numerous start-ups around the world have started receiving funding from government agencies and venture capitalists. This fuels the adoption of privacy-preserving data computation techniques in real application and the field is rapidly evolving. Researchers and students studying/working in data protection and related security fields will find this book useful as a reference.

Download or read book Machine Learning and Cryptographic Solutions for Data Protection and Network Security written by Ruth, J. Anitha and published by IGI Global. This book was released on 2024-05-31 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the relentless battle against escalating cyber threats, data security faces a critical challenge – the need for innovative solutions to fortify encryption and decryption processes. The increasing frequency and complexity of cyber-attacks demand a dynamic approach, and this is where the intersection of cryptography and machine learning emerges as a powerful ally. As hackers become more adept at exploiting vulnerabilities, the book stands as a beacon of insight, addressing the urgent need to leverage machine learning techniques in cryptography. Machine Learning and Cryptographic Solutions for Data Protection and Network Security unveil the intricate relationship between data security and machine learning and provide a roadmap for implementing these cutting-edge techniques in the field. The book equips specialists, academics, and students in cryptography, machine learning, and network security with the tools to enhance encryption and decryption procedures by offering theoretical frameworks and the latest empirical research findings. Its pages unfold a narrative of collaboration and cross-pollination of ideas, showcasing how machine learning can be harnessed to sift through vast datasets, identify network weak points, and predict future cyber threats.

Download or read book Cyber Security Meets Machine Learning written by Xiaofeng Chen and published by Springer Nature. This book was released on 2021-07-02 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

Download or read book Machine Learning and Knowledge Discovery in Databases written by Michelangelo Ceci and published by Springer. This book was released on 2017-12-29 with total page 881 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.

Download or read book Technology and Privacy written by Philip Agre and published by MIT Press. This book was released on 1998 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last several years, the realm of technology and privacy has been transformed, creating a landscape that is both dangerous and encouraging. Significant changes include large increases in communications bandwidths; the widespread adoption of computer networking and public-key cryptography; new digital media that support a wide range of social relationships; a massive body of practical experience in the development and application of data-protection laws; and the rapid globalization of manufacturing, culture, and policy making. The essays in this book provide a new conceptual framework for the analysis and debate of privacy policy and for the design and development of information systems.

Download or read book Privacy Preserving Machine Learning written by J. Morris Chang and published by Simon and Schuster. This book was released on 2023-05-02 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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)

Download or read book Data Analysis Machine Learning and Knowledge Discovery written by Myra Spiliopoulou and published by Springer Science & Business Media. This book was released on 2013-11-26 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012. ​

Download or read book Machine Learning and Data Mining for Computer Security written by Marcus A. Maloof and published by Springer Science & Business Media. This book was released on 2006-02-27 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This book has a strong focus on information processing and combines and extends results from computer security. The first part of the book surveys the data sources, the learning and mining methods, evaluation methodologies, and past work relevant for computer security. The second part of the book consists of articles written by the top researchers working in this area. These articles deals with topics of host-based intrusion detection through the analysis of audit trails, of command sequences and of system calls as well as network intrusion detection through the analysis of TCP packets and the detection of malicious executables. This book fills the great need for a book that collects and frames work on developing and applying methods from machine learning and data mining to problems in computer security.

Download or read book The Knowledge Machine How Irrationality Created Modern Science written by Michael Strevens and published by Liveright Publishing. This book was released on 2020-10-13 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: “The Knowledge Machine is the most stunningly illuminating book of the last several decades regarding the all-important scientific enterprise.” —Rebecca Newberger Goldstein, author of Plato at the Googleplex A paradigm-shifting work, The Knowledge Machine revolutionizes our understanding of the origins and structure of science. • Why is science so powerful? • Why did it take so long—two thousand years after the invention of philosophy and mathematics—for the human race to start using science to learn the secrets of the universe? In a groundbreaking work that blends science, philosophy, and history, leading philosopher of science Michael Strevens answers these challenging questions, showing how science came about only once thinkers stumbled upon the astonishing idea that scientific breakthroughs could be accomplished by breaking the rules of logical argument. Like such classic works as Karl Popper’s The Logic of Scientific Discovery and Thomas Kuhn’s The Structure of Scientific Revolutions, The Knowledge Machine grapples with the meaning and origins of science, using a plethora of vivid historical examples to demonstrate that scientists willfully ignore religion, theoretical beauty, and even philosophy to embrace a constricted code of argument whose very narrowness channels unprecedented energy into empirical observation and experimentation. Strevens calls this scientific code the iron rule of explanation, and reveals the way in which the rule, precisely because it is unreasonably close-minded, overcomes individual prejudices to lead humanity inexorably toward the secrets of nature. “With a mixture of philosophical and historical argument, and written in an engrossing style” (Alan Ryan), The Knowledge Machine provides captivating portraits of some of the greatest luminaries in science’s history, including Isaac Newton, the chief architect of modern science and its foundational theories of motion and gravitation; William Whewell, perhaps the greatest philosopher-scientist of the early nineteenth century; and Murray Gell-Mann, discoverer of the quark. Today, Strevens argues, in the face of threats from a changing climate and global pandemics, the idiosyncratic but highly effective scientific knowledge machine must be protected from politicians, commercial interests, and even scientists themselves who seek to open it up, to make it less narrow and more rational—and thus to undermine its devotedly empirical search for truth. Rich with illuminating and often delightfully quirky illustrations, The Knowledge Machine, written in a winningly accessible style that belies the import of its revisionist and groundbreaking concepts, radically reframes much of what we thought we knew about the origins of the modern world.

Download or read book Proceedings of International Conference on Intelligent Computing Information and Control Systems written by A. Pasumpon Pandian and published by Springer Nature. This book was released on 2021-01-24 with total page 972 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of papers presented at the International Conference on Intelligent Computing, Information and Control Systems (ICICCS 2020). It encompasses various research works that help to develop and advance the next-generation intelligent computing and control systems. The book integrates the computational intelligence and intelligent control systems to provide a powerful methodology for a wide range of data analytics issues in industries and societal applications. The book also presents the new algorithms and methodologies for promoting advances in common intelligent computing and control methodologies including evolutionary computation, artificial life, virtual infrastructures, fuzzy logic, artificial immune systems, neural networks and various neuro-hybrid methodologies. This book is pragmatic for researchers, academicians and students dealing with mathematically intransigent problems.