Download or read book Towards Ethical and Robust Privacy preserving Machine Learning written by Hui Hu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Privacy in machine learning has received tremendous attention in recent years, which mainly involves data privacy and model privacy. Recent studies have revealed numerous privacy attacks and privacy-preserving methodologies, that vary across a broad range of applications. To date, however, there exist few powerful methodologies in addressing privacy-preserving challenges in ethical machine learning and deep learning due to the difficulty of guaranteeing model robustness and privacy-preserving simultaneously. In this dissertation, two critical problems will be investigated and addressed: data privacy-preserving in ethical machine learning, and model privacy-preserving in deep learning under powerful side-channel power attacks. First, we investigate the problem of data privacy-preserving in ethical machine learning with the following two considerations: (1) Users’ privacy (i.e., race, religion, gender, etc.) is severely leaked in ethical machine learning as most existing techniques require full access to sensitive personal data to achieve model fairness. To address this pressing privacy issue, we propose a distributed privacy-preserving fair machine learning mechanism based on random projection theory and multi-party computation. Through rigorous theoretical analysis and comprehensive simulations, we can prove that the proposed mechanism is efficient for privacy-preserving while guaranteeing good model robustness. Further, (2) considering the dependency relation of graph data in ethical machine learning, an individual’s privacy can be leaked due to the sensitive information disclosure of their neighbors. Typically, in a graph neural network, the sensitive information disclosure of non-private users potentially exposes the sensitive information of private users in the same graph owing to the homophily property and message-passing mechanism of graph neural networks. To address this problem, based on disentangled representation learning, we propose a principled privacy-preserving graph neural network model to mitigate individual privacy leakage of private users in a graph, which maintains competitive model accuracy compared with non-private graph neural networks. We verify the effectiveness of the proposed privacy-preserving model through extensive experiments and theoretical analysis. Second, as the disclosure of model privacy can allow adversaries to potentially infer users’ extremely sensitive decisions, further, we study model privacy-preserving in deep learning under side-channel power attacks. Side-channel power attacks are powerful attacks that infer the internal information of a traditional deep neural network (i.e., model privacy), which can be leveraged to infer some important decisions of users. Therefore, with the increasing applications of deep learning, training privacy-preserving deep neural networks under side-channel power attacks is a pressing task. This dissertation proposes an efficient solution for training privacy-preserving deep neural networks to resist powerful side-channel power attacks, which randomly trains multiple independent sub-networks to generate random power traces in the temporal domain. The comprehensive theoretical analysis and experimental results demonstrate the effectiveness of the proposed approach in model privacy-preserving and model robustness under side-channel power attacks.

Download or read book Federated Learning and Privacy Preserving in Healthcare AI written by Lilhore, Umesh Kumar and published by IGI Global. This book was released on 2024-05-02 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI.

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

Download or read book Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security written by Indrajit Ray and published by . This book was released on 2015-10-12 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: CCS'15: The 22nd ACM Conference on Computer and Communications Security Oct 12, 2015-Oct 16, 2015 Denver, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.

Download or read book Robust and Privacy Preserving Distributed Machine Learning written by Rania Talbi and published by . This book was released on 2021 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the pervasiveness of digital services, huge amounts of data are nowadays continuously generated and collected. Machine Learning (ML) algorithms allow the extraction of hidden yet valuable knowledge from these data and have been applied in numerous domains, such as health care assistance, transportation, user behavior prediction, and many others. In many of these applications, data is collected from different sources and distributed training is required to learn global models over them. However, in the case of sensitive data, running traditional ML algorithms over them can lead to serious privacy breaches by leaking sensitive information about data owners and data users. In this thesis, we propose mechanisms allowing to enhance privacy preservation and robustness in the domain of distributed machine learning. The first contribution of this thesis falls in the category of cryptography-based privacy preserving machine learning. Many state-of-the-art works propose cryptography-based solutions to ensure privacy preservation in distributed machine learning. Nonetheless, these works are known to induce huge overheads time and space-wise. In this line of works, we propose PrivML an outsourced Homomorphic Encryption-based Privacy Preserving Collaborative Machine Learning framework, that allows optimizing runtime and bandwidth consumption for widely used ML algorithms, using many techniques such as ciphertext packing, approximate computations, and parallel computing. The other contributions of this thesis address the robustness issues in the domain of Federated Learning. Indeed federated learning is the first framework to ensure privacy by design for distributed machine learning. Nonetheless, it has been shown that this framework is still vulnerable to many attacks, among them we find poisoning attacks, where participants deliberately use faulty training data to provoke misclassification at inference time. We demonstrate that state-of-the-art poisoning mitigation mechanisms fail to detect some poisoning attacks and propose ARMOR, a poisoning mitigation mechanism for Federated Learning that successfully detects these attacks, without hurting models' utility.

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)

Download or read book Towards Ethical and Socially Responsible Explainable AI written by Mohammad Amir Khusru Akhtar and published by Springer Nature. This book was released on with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Robust and Privacy Preserving Federated Learning written by Fatima Elhattab and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today's rapidly evolving digital landscape, machine learning has become an in- dispensable and transformative force, as substantiated by extensive research studies. Its profound impact spans across diverse industries, offering ground breaking solutions and innovations that have reshaped the way we interact with technology and make decisions. From recommendation systems enhancing content delivery on platforms to the presence of virtual personal assistants like Siri and Alexa, capable of understanding and responding to natural language commands, the applications of machine learning are both diverse and impactful. In domains like healthcare, it aids in disease diagnosis, while in finance, it fortifies fraud detection and risk assessment. This ubiquity of machine learning signifies not just a technological trend but a fundamental shift in problem-solving and decision-making approaches. However, this surge in data-driven innovation has raised a paramount concern - the protection of individuals' privacy and personal data. The General Data Protection Regulation (GDPR) exemplifies the heightened importance of data privacy in our modern era. As machine learning becomes increasingly intertwined with our daily lives, achieving a delicate balance between technological advancements and safeguarding individual privacy has become imperative. Moreover, addressing these concerns has given rise to the concept of privacy-preserving machine learning, with federated learning emerging as a pivotal technique, redefining collaborative machine learning by enabling multiple parties to build a shared model without sharing their raw data. Federated Learning represents a promising paradigm in Machine Learning, enabling collaborative model training among decentralized devices in edge computing systems. However, it exhibits susceptibility to various attacks. This research is divided into two main thrusts, each addressing critical security and privacy challenges in the context of Federated Learning. The first thrust focuses on countering poisoning attacks for robust Federated Learning, where adversaries aim to introduce harmful tasks into federated models alongside their main tasks. To detect these attacks, the research introduces ARMOR, a novel GAN-based attack detection system that analyzes the information embedded in model updates. The second thrust deals with countering inference attacks for privacy-preserving Federated Learning, specifically membership inference attacks. To bolster privacy in FL, two novel approaches are introduced: PASTEL, which enhances FL systems' resilience against MIAs by minimizing the internal generalization gap, and DINAR, a fine-grained privacy-preserving FL method that obfuscates privacy-sensitive layers and employs adaptive gradient descent to enhance model utility. These research objectives collectively aim to address security and privacy challenges and advance the field of federated learning.

Download or read book Advanced Machine Learning written by Dr. Amit Kumar Tyagi and published by BPB Publications. This book was released on 2024-06-29 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: DESCRIPTION Our book is divided into several useful concepts and techniques of machine learning. This book serves as a valuable resource for individuals seeking to deepen their understanding of advanced topics in this field. Learn about various learning algorithms, including supervised, unsupervised, and reinforcement learning, and their mathematical foundations. Discover the significance of feature engineering and selection for enhancing model performance. Understand model evaluation metrics like accuracy, precision, recall, and F1-score, along with techniques like cross-validation and grid search for model selection. Explore ensemble learning methods along with deep learning, unsupervised learning, time series analysis, and reinforcement learning techniques. Lastly, uncover real-world applications of the machine and deep learning algorithms. After reading this book, readers will gain a comprehensive understanding of machine learning fundamentals and advanced techniques. With this knowledge, readers will be equipped to tackle real-world problems, make informed decisions, and develop innovative solutions using machine and deep learning algorithms. KEY FEATURES ● Basic understanding of machine learning algorithms via MATLAB, R, and Python. ● Inclusion of examples related to real-world problems, case studies, and questions related to futuristic technologies. ● Adding futuristic technologies related to machine learning and deep learning. WHAT YOU WILL LEARN ● Ability to tackle complex machine learning problems. ● Understanding of foundations, algorithms, ethical issues, and how to implement each learning algorithm for their own use/ with their data. ● Efficient data analysis for real-time data will be understood by researchers/ students. ● Using data analysis in near future topics and cutting-edge technologies. WHO THIS BOOK IS FOR This book is ideal for students, professors, and researchers. It equips industry experts and academics with the technical know-how and practical implementations of machine learning algorithms. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Statistical Analysis 3. Linear Regression 4. Logistic Regression 5. Decision Trees 6. Random Forest 7. Rule-Based Classifiers 8. Naïve Bayesian Classifier 9. K-Nearest Neighbors Classifiers 10. Support Vector Machine 11. K-Means Clustering 12. Dimensionality Reduction 13. Association Rules Mining and FP Growth 14. Reinforcement Learning 15. Applications of ML Algorithms 16. Applications of Deep Learning 17. Advance Topics and Future Directions

Download or read book Deep Learning written by Rob Botwright and published by Rob Botwright. This book was released on 101-01-01 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing the Ultimate AI Book Bundle: Deep Learning, Computer Vision, Python Machine Learning, and Neural Networks Are you ready to embark on an exhilarating journey into the world of artificial intelligence, deep learning, and computer vision? Look no further! Our carefully curated book bundle, "DEEP LEARNING: COMPUTER VISION, PYTHON MACHINE LEARNING AND NEURAL NETWORKS," offers you a comprehensive roadmap to AI mastery. BOOK 1 - DEEP LEARNING DEMYSTIFIED: A BEGINNER'S GUIDE 🚀 Perfect for beginners, this book dismantles the complexities of deep learning. From neural networks to Python programming, you'll build a strong foundation in AI. BOOK 2 - MASTERING COMPUTER VISION WITH DEEP LEARNING 🌟 Dive into the captivating world of computer vision. Unlock the secrets of image processing, convolutional neural networks (CNNs), and object recognition. Harness the power of visual intelligence! BOOK 3 - PYTHON MACHINE LEARNING AND NEURAL NETWORKS: FROM NOVICE TO PRO 📊 Elevate your skills with this intermediate volume. Delve into data preprocessing, supervised and unsupervised learning, and become proficient in training neural networks. BOOK 4 - ADVANCED DEEP LEARNING: CUTTING-EDGE TECHNIQUES AND APPLICATIONS 🔥 Ready to conquer advanced techniques? Learn optimization strategies, tackle common deep learning challenges, and explore real-world applications shaping the future. 🎉 What You'll Gain: · A strong foundation in deep learning · Proficiency in computer vision · Mastery of Python machine learning · Advanced deep learning skills · Real-world application knowledge · Cutting-edge AI insights 📚 Why Choose Our Book Bundle? · Expertly curated content · Beginner to expert progression · Clear explanations and hands-on examples · Comprehensive coverage of AI topics · Practical real-world applications · Stay ahead with emerging AI trends 🌐 Who Should Grab This Bundle? · Beginners eager to start their AI journey · Intermediate learners looking to expand their skill set · Experts seeking advanced deep learning insights · Anyone curious about AI's limitless possibilities 📦 Limited-Time Offer: Get all four books in one bundle and save! Don't miss this chance to accelerate your AI knowledge and skills. 🔒 Secure Your AI Mastery: Click "Add to Cart" now and embark on an educational adventure that will redefine your understanding of artificial intelligence. Your journey to AI excellence begins here!

Download or read book Trustworthy Machine Learning for Healthcare written by Hao Chen and published by Springer Nature. This book was released on 2023-07-30 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of First International Workshop, TML4H 2023, held virtually, in May 2023. The 16 full papers included in this volume were carefully reviewed and selected from 30 submissions. The goal of this workshop is to bring together experts from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability.

Download or read book Ethik der Kindheit written by Avinash Manure and published by Apress. This book was released on 2023-11-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn and implement responsible AI models using Python. This book will teach you how to balance ethical challenges with opportunities in artificial intelligence. The book starts with an introduction to the fundamentals of AI, with special emphasis given to the key principles of responsible AI. The authors then walk you through the critical issues of detecting and mitigating bias, making AI decisions understandable, preserving privacy, ensuring security, and designing robust models. Along the way, you’ll gain an overview of tools, techniques, and code examples to implement the key principles you learn in real-world scenarios. The book concludes with a chapter devoted to fostering a deeper understanding of responsible AI’s profound implications for the future. Each chapter offers a hands-on approach, enriched with practical insights and code snippets, enabling you to translate ethical considerations into actionable solutions. What You Will Learn Understand the principles of responsible AI and their importance in today's digital world Master techniques to detect and mitigate bias in AI Explore methods and tools for achieving transparency and explainability Discover best practices for privacy preservation and security in AI Gain insights into designing robust and reliable AI models Who This Book Is For AI practitioners, data scientists, machine learning engineers, researchers, policymakers, and students interested in the ethical aspects of AI

Download or read book Algorithms of Intelligence Exploring the World of Machine Learning written by Dr R. Keerthika and published by Inkbound Publishers. This book was released on 2022-01-20 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delve into the fascinating world of machine learning with this comprehensive guide, which unpacks the algorithms driving today's intelligent systems. From foundational concepts to advanced applications, this book is essential for anyone looking to understand the mechanics behind AI.

Download or read book Strategies for E Commerce Data Security Cloud Blockchain AI and Machine Learning written by Goel, Pawan Kumar and published by IGI Global. This book was released on 2024-08-22 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the landscape of e-commerce, data security has become a concern as businesses navigate the complexities of sensitive customer information protection and cyber threat mitigation. Strategies involving cloud computing, blockchain technology, artificial intelligence, and machine learning offer solutions to strengthen data security and ensure transactional integrity. Implementing these technologies requires a balance of innovation and efficient security protocols. The development and adoption of security strategies is necessary to positively integrate cutting-edge technologies for effective security in online business. Strategies for E-Commerce Data Security: Cloud, Blockchain, AI, and Machine Learning addresses the need for advanced security measures, while examining the current state of e-commerce data security. It explores strategies such as cloud computing, blockchain, artificial intelligence, and machine learning. This book covers topics such as cybersecurity, cloud technology, and forensics, and is a useful resource for computer engineers, business owners, security professionals, government officials, academicians, scientists, and researchers.

Download or read book ICT Applications and Social Interfaces written by Amit Joshi and published by Springer Nature. This book was released on with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-08-22 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.