Privacy Preservation In Iot Machine Learning Approaches

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Privacy Preservation in IoT: Machine Learning Approaches

Author : Youyang Qu
Publisher : Springer Nature
ISBN 13 : 9811917973
Total Pages : 127 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Privacy Preservation in IoT: Machine Learning Approaches by : Youyang Qu

Download or read book Privacy Preservation in IoT: Machine Learning Approaches written by Youyang Qu and published by Springer Nature. This book was released on 2022-04-27 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Learning Techniques for IoT Security and Privacy

Author : Mohamed Abdel-Basset
Publisher : Springer Nature
ISBN 13 : 3030890252
Total Pages : 273 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Deep Learning Techniques for IoT Security and Privacy by : Mohamed Abdel-Basset

Download or read book Deep Learning Techniques for IoT Security and Privacy written by Mohamed Abdel-Basset and published by Springer Nature. This book was released on 2021-12-05 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning. Interpretation of statistics and probability theory will be a plus but is not certainly vital for identifying most of the book's material.

Deep Learning Approaches for Security Threats in IoT Environments

Author : Mohamed Abdel-Basset
Publisher : John Wiley & Sons
ISBN 13 : 1119884144
Total Pages : 388 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Deep Learning Approaches for Security Threats in IoT Environments by : Mohamed Abdel-Basset

Download or read book Deep Learning Approaches for Security Threats in IoT Environments written by Mohamed Abdel-Basset and published by John Wiley & Sons. This book was released on 2022-12-20 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning Approaches for Security Threats in IoT Environments An expert discussion of the application of deep learning methods in the IoT security environment In Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation. This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues. Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They’ll also find: A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacy Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks In-depth examinations of the architectural design of cloud, fog, and edge computing networks Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networks Perfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks.

Privacy-Preserving Machine Learning

Author : J. Morris Chang
Publisher : Simon and Schuster
ISBN 13 : 1638352755
Total Pages : 334 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Privacy-Preserving Machine Learning by : J. Morris Chang

Download or read book Privacy-Preserving Machine Learning written by J. Morris Chang and published by Simon and Schuster. This book was released on 2023-05-23 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. 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)

Deep Learning for Security and Privacy Preservation in IoT

Author : Aaisha Makkar
Publisher : Springer Nature
ISBN 13 : 9811661863
Total Pages : 186 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Deep Learning for Security and Privacy Preservation in IoT by : Aaisha Makkar

Download or read book Deep Learning for Security and Privacy Preservation in IoT written by Aaisha Makkar and published by Springer Nature. This book was released on 2022-04-03 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the issues with privacy and security in Internet of things (IoT) networks which are susceptible to cyber-attacks and proposes deep learning-based approaches using artificial neural networks models to achieve a safer and more secured IoT environment. Due to the inadequacy of existing solutions to cover the entire IoT network security spectrum, the book utilizes artificial neural network models, which are used to classify, recognize, and model complex data including images, voice, and text, to enhance the level of security and privacy of IoT. This is applied to several IoT applications which include wireless sensor networks (WSN), meter reading transmission in smart grid, vehicular ad hoc networks (VANET), industrial IoT and connected networks. The book serves as a reference for researchers, academics, and network engineers who want to develop enhanced security and privacy features in the design of IoT systems.

Privacy-Preserving Deep Learning

Author : Kwangjo Kim
Publisher : Springer Nature
ISBN 13 : 9811637644
Total Pages : 81 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Privacy-Preserving Deep Learning by : Kwangjo Kim

Download or read book Privacy-Preserving Deep Learning written by Kwangjo Kim and published by Springer Nature. This book was released on 2021-07-22 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Privacy-Preserving Machine Learning

Author : Srinivasa Rao Aravilli
Publisher : Packt Publishing Ltd
ISBN 13 : 1800564228
Total Pages : 402 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Privacy-Preserving Machine Learning by : Srinivasa Rao Aravilli

Download or read book Privacy-Preserving Machine Learning written by Srinivasa Rao Aravilli and published by Packt Publishing Ltd. This book was released on 2024-05-24 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Description– In an era of evolving privacy regulations, compliance is mandatory for every enterprise – Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information – This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases – As you progress, you’ll be guided through developing anti-money laundering solutions using federated learning and differential privacy – Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models – You’ll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field – Upon completion, you’ll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks 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 comprehensive guide is for data scientists, machine learning engineers, and privacy engineers – Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn) – Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques

Privacy Preserving Data Mining

Author : Jaideep Vaidya
Publisher : Springer Science & Business Media
ISBN 13 : 0387294899
Total Pages : 124 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Privacy Preserving Data Mining by : Jaideep Vaidya

Download or read book Privacy Preserving Data Mining written by Jaideep Vaidya and published by Springer Science & Business Media. This book was released on 2006-09-28 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Security and Privacy Preserving for IoT and 5G Networks

Author : Ahmed A. Abd El-Latif
Publisher : Springer Nature
ISBN 13 : 3030854280
Total Pages : 283 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Security and Privacy Preserving for IoT and 5G Networks by : Ahmed A. Abd El-Latif

Download or read book Security and Privacy Preserving for IoT and 5G Networks written by Ahmed A. Abd El-Latif and published by Springer Nature. This book was released on 2021-10-09 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Federated Learning and Its Role in the Privacy Preservation of IoT Devices

Author : Tanweer Alam
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Federated Learning and Its Role in the Privacy Preservation of IoT Devices by : Tanweer Alam

Download or read book Federated Learning and Its Role in the Privacy Preservation of IoT Devices written by Tanweer Alam and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results.

Security and Quality in Cyber-Physical Systems Engineering

Author : Stefan Biffl
Publisher : Springer Nature
ISBN 13 : 3030253120
Total Pages : 507 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Security and Quality in Cyber-Physical Systems Engineering by : Stefan Biffl

Download or read book Security and Quality in Cyber-Physical Systems Engineering written by Stefan Biffl and published by Springer Nature. This book was released on 2019-11-09 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the requirements, risks, and solutions to improve the security and quality of complex cyber-physical systems (C-CPS), such as production systems, power plants, and airplanes, in order to ascertain whether it is possible to protect engineering organizations against cyber threats and to ensure engineering project quality. The book consists of three parts that logically build upon each other. Part I "Product Engineering of Complex Cyber-Physical Systems" discusses the structure and behavior of engineering organizations producing complex cyber-physical systems, providing insights into processes and engineering activities, and highlighting the requirements and border conditions for secure and high-quality engineering. Part II "Engineering Quality Improvement" addresses quality improvements with a focus on engineering data generation, exchange, aggregation, and use within an engineering organization, and the need for proper data modeling and engineering-result validation. Lastly, Part III "Engineering Security Improvement" considers security aspects concerning C-CPS engineering, including engineering organizations’ security assessments and engineering data management, security concepts and technologies that may be leveraged to mitigate the manipulation of engineering data, as well as design and run-time aspects of secure complex cyber-physical systems. The book is intended for several target groups: it enables computer scientists to identify research issues related to the development of new methods, architectures, and technologies for improving quality and security in multi-disciplinary engineering, pushing forward the current state of the art. It also allows researchers involved in the engineering of C-CPS to gain a better understanding of the challenges and requirements of multi-disciplinary engineering that will guide them in their future research and development activities. Lastly, it offers practicing engineers and managers with engineering backgrounds insights into the benefits and limitations of applicable methods, architectures, and technologies for selected use cases.

Research Anthology on Privatizing and Securing Data

Author : Management Association, Information Resources
Publisher : IGI Global
ISBN 13 : 1799889556
Total Pages : 2188 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Research Anthology on Privatizing and Securing Data by : Management Association, Information Resources

Download or read book Research Anthology on Privatizing and Securing Data written by Management Association, Information Resources and published by IGI Global. This book was released on 2021-04-23 with total page 2188 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Intelligent IoT for the Digital World

Author : Yang Yang
Publisher : John Wiley & Sons
ISBN 13 : 1119593565
Total Pages : 340 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Intelligent IoT for the Digital World by : Yang Yang

Download or read book Intelligent IoT for the Digital World written by Yang Yang and published by John Wiley & Sons. This book was released on 2021-04-08 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: INTELLIGENT IOT FOR THE DIGITAL WORLD DISCOVER HOW THE INTELLIGENT INTERNET OF THINGS WILL CHANGE THE INFORMATION AND COMMUNICATION TECHNOLOGY INDUSTRY IN THE NEXT DECADE In the digital world, most data and Internet of Things (IoT) services need to be efficiently processed and executed by intelligent algorithms using local or regional computing resources, thus greatly saving and reducing communication bandwidth, end-to-end service delay, long-distance data transmissions, and potential privacy breaches. This book proposes a pyramid model, where data, computing and algorithm jointly constitute the triangular base to support a variety of user-centric intelligent IoT services at the spire by using different kinds of smart terminals or devices. This book provides a state-of-the-art review of intelligent IoT technologies and applications, discusses the key challenges and opportunities facing the digital world, and answers the following five critical questions: What is the most feasible network architecture to effectively provide sufficient resources anywhere anytime for intelligent IoT application scenarios? How do we efficiently discover, allocate and manage computing, communication and caching resources in heterogeneous networks across multiple domains and operators? How do we agilely achieve adaptive service orchestration and reliable service provisioning to meet dynamic user requirements in real time? How do we effectively protect data privacy in IoT applications, where IoT devices and edge/fog computing nodes only have limited resources and capabilities? How do we continuously guarantee and maintain the synchronization and reliability of wide-area IoT systems and applications? Written for professionals working in 5G/IoT technology development, service management and big data analytics, this book offers an overview of intelligent IoT service architecture, key technologies, important applications and future technological trends.

IoT Security Paradigms and Applications

Author : Sudhir Kumar Sharma
Publisher : CRC Press
ISBN 13 : 1000172244
Total Pages : 338 pages
Book Rating : 4.0/5 (1 download)

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Book Synopsis IoT Security Paradigms and Applications by : Sudhir Kumar Sharma

Download or read book IoT Security Paradigms and Applications written by Sudhir Kumar Sharma and published by CRC Press. This book was released on 2020-10-07 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integration of IoT (Internet of Things) with big data and cloud computing has brought forward numerous advantages and challenges such as data analytics, integration, and storage. This book highlights these challenges and provides an integrating framework for these technologies, illustrating the role of blockchain in all possible facets of IoT security. Furthermore, it investigates the security and privacy issues associated with various IoT systems along with exploring various machine learning-based IoT security solutions. This book brings together state-of-the-art innovations, research activities (both in academia and in industry), and the corresponding standardization impacts of 5G as well. Aimed at graduate students, researchers in computer science and engineering, communication networking, IoT, machine learning and pattern recognition, this book Showcases the basics of both IoT and various security paradigms supporting IoT, including Blockchain Explores various machine learning-based IoT security solutions and highlights the importance of IoT for industries and smart cities Presents various competitive technologies of Blockchain, especially concerned with IoT security Provides insights into the taxonomy of challenges, issues, and research directions in IoT-based applications Includes examples and illustrations to effectively demonstrate the principles, algorithm, applications, and practices of security in the IoT environment

Machine Learning, Big Data, and IoT for Medical Informatics

Author : Pardeep Kumar
Publisher : Academic Press
ISBN 13 : 0128217812
Total Pages : 458 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Machine Learning, Big Data, and IoT for Medical Informatics by : Pardeep Kumar

Download or read book Machine Learning, Big Data, and IoT for Medical Informatics written by Pardeep Kumar and published by Academic Press. This book was released on 2021-06-13 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT. Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems. Includes several privacy preservation techniques for medical data. Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis. Offers case studies and applications relating to machine learning, big data, and health care analysis.

Machine Learning Approach for Cloud Data Analytics in IoT

Author : Sachi Nandan Mohanty
Publisher : John Wiley & Sons
ISBN 13 : 1119785804
Total Pages : 530 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Machine Learning Approach for Cloud Data Analytics in IoT by : Sachi Nandan Mohanty

Download or read book Machine Learning Approach for Cloud Data Analytics in IoT written by Sachi Nandan Mohanty and published by John Wiley & Sons. This book was released on 2021-07-27 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.

Towards a Complete Privacy Preserving Machine Learning Pipeline

Author : Ali Burak Ünal
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Towards a Complete Privacy Preserving Machine Learning Pipeline by : Ali Burak Ünal

Download or read book Towards a Complete Privacy Preserving Machine Learning Pipeline written by Ali Burak Ünal and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has proven its success on various problems from many different domains. Different machine learning algorithms use different approaches to capture the underlying patterns in the data. Even though the amount varies between the machine learning algorithms, they require sufficient amounts of data to recognize those patterns. One of the easiest ways to meet this need of the machine learning algorithms is to use multiple sources generating the same type of data. Such a solution is feasible considering that the speed of data generation and the number of sources generating these data have been increasing in parallel to the developments in technology. One can easily satisfy the desire of the machine learning algorithms for data using these sources. However, this can cause a privacy leakage. The data generated by these sources may contain sensitive information that can be used for undesirable purposes. Therefore, although the machine learning algorithms demand for data, the sources may not be willing or even allowed to share their data. A similar dilemma occurs when the data owner wants to extract useful information from the data by using machine learning algorithms but it does not have enough computational power or knowledge. In this case, the data source may want to outsource this task to external parties that offer machine learning algorithms as a service. Similarly, in this case, the sensitive information in the data can be the decisive factor for the owner not to choose outsourcing, which then ends up with non-utilized data for the owner. In order to address these kinds of dilemmas and issues, this thesis aims to come up with a complete privacy preserving machine learning pipeline. It introduces several studies that address different phases of the pipeline so that all phases of a machine learning algorithm can be performed privately. One of these phases addressed in this thesis is training of a machine learning algorithm. The privacy preserving training of kernel-based machine learning algorithms are addressed in several different works with different cryptographic techniques, one of which is a our newly developed encryption scheme. The different techniques have different advantages over the others. Furthermore, this thesis introduces our study addressing the testing phase of not only the kernel-based machine learning algorithms but also a special type of recurrent neural network, namely recurrent kernel networks, which is the first study performing such an inference, without compromising privacy. To enable the privacy preserving inference on recurrent kernel networks, this thesis introduces a framework, called CECILIA, with two novel functions, which are the exponential and the inverse square root of the Gram matrix, and efficient versions of the existing functions, which are the multiplexer and the most significant bit. Using this framework and other approaches in the corresponding studies, it is possible to perform privacy preserving inference on various pre-trained machine learning algorithms. Besides the training and testing of machine learning algorithms in a privacy preserving way, this thesis also presents a work that aims to evaluate the performance of machine learning algorithms without sacrificing privacy. This work employs CECILIA to realize the area under curve calculation for two different curve-based evaluations, namely the receiver operating characteristic curve and the precision-recall curve, in a privacy preserving manner. All the proposed approaches are shown to be correct using several machine learning tasks and evaluated for the scalability of the parameters of the corresponding system/algorithm using synthetic data. The results show that the privacy preserving training and testing of kernel-based machine learning algorithms is possible with different settings and the privacy preserving inference on a pre-trained recurrent kernel network is feasible using CECILIA. Additionally, CECILIA also allows the exact area under curve computation to evaluate the performance of a machine learning algorithm without compromising privacy.