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:

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 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 Privacy Preserving Machine Learning written by Srinivasa Rao Aravilli and published by Packt Publishing. This book was released on 2023-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book helps software engineers, data scientists, ML and AI engineers, and research and development teams to learn and implement privacy-preserving machine learning as well as protect companies against privacy breaches.

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 Building Machine Learning Pipelines written by Hannes Hapke and published by O'Reilly Media. This book was released on 2020-07-13 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

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.

Download or read book Towards Effective Efficient and Equitable Privacy preserving Machine Learning written by Nitin Agrawal and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Towards Privacy Preserving Micro data Analysis written by Navoda Senavirathne and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Towards Efficient and Effective Privacy Preserving Machine Learning written by Lingxiao Wang and published by . This book was released on 2021 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has witnessed the fast growth and tremendous success of machine learning. However, recent studies showed that existing machine learning models are vulnerable to privacy attacks, such as membership inference attacks, and thus pose severe threats to personal privacy. Therefore, one of the major challenges in machine learning is to learn effectively from enormous amounts of sensitive data without giving up on privacy. This dissertation summarizes our contributions to the field of privacy-preserving machine learning, i.e., solving machine learning problems with strong privacy and utility guarantees. In the first part of the dissertation, we consider the privacy-preserving sparse learning problem. More specifically, we establish a novel differentially private hard-thresholding method as well as a knowledge-transfer framework for solving the sparse learning problem. We show that our proposed methods are not only efficient but can also achieve improved privacy and utility guarantees. In the second part of the dissertation, we propose novel efficient and effective algorithms for solving empirical risk minimization problems. To be more specific, our proposed algorithms can reduce the computational complexities and improve the utility guarantees for solving nonconvex optimization problems such as training deep neural networks. In the last part of the dissertation, we study the privacy-preserving empirical risk minimization in the distributed setting. In such a setting, we propose a new privacy-preserving framework by combining the multi-party computation (MPC) protocol and differentially private mechanisms and show that our framework can achieve better privacy and utility guarantees compared with existing methods. The methods and techniques proposed in this dissertation form a line of researches that deepens our understandings of the trade-off between privacy, utility and efficient in privacy-preserving machine learning, and could also help us develop more efficient and effective private learning algorithms.

Download or read book Privacy preserving Computing written by Kai Chen and published by Cambridge University Press. This book was released on 2023-11-30 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: Systematically introduces privacy-preserving computing techniques and practical applications for students, researchers, and practitioners.

Download or read book Privacy Preserving Machine Learning written by Jin Li and published by Springer. This book was released on 2022-03-15 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the evolution of privacy-preserving machine learning schemes over the last ten years, after discussing the importance of privacy-preserving techniques. In response to the diversity of Internet services, data services based on machine learning are now available for various applications, including risk assessment and image recognition. In light of open access to datasets and not fully trusted environments, machine learning-based applications face enormous security and privacy risks. In turn, it presents studies conducted to address privacy issues and a series of proposed solutions for ensuring privacy protection in machine learning tasks involving multiple parties. In closing, the book reviews state-of-the-art privacy-preserving techniques and examines the security threats they face.

Download or read book Practical Data Privacy written by Katharine Jarmul and published by "O'Reilly Media, Inc.". This book was released on 2023-04-19 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help? How do I ensure that my data science projects are secure by default and private by design? How do I work with governance and infosec teams to implement internal policies appropriately?

Download or read book Privacy and Security Issues in Data Mining and Machine Learning written by Christos Dimitrakakis and published by Springer Science & Business Media. This book was released on 2011-03-17 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International ECML/PKDD Workshop on Privacy and Security Issues in Data Mining and Machine Learning, PSDML 2010, held in Barcelona, Spain, in September 2010. The 11 revised full papers presented were carefully reviewed and selected from 21 submissions. The papers range from data privacy to security applications, focusing on detecting malicious behavior in computer systems.

Download or read book Building Machine Learning Pipelines written by Hannes Hapke and published by O'Reilly Media. This book was released on 2020-09-08 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. The book also explores new approaches for integrating data privacy into machine learning pipelines. Understand the machine learning management lifecycle Implement data pipelines with Apache Airflow and Kubeflow Pipelines Work with data using TensorFlow tools like ML Metadata, TensorFlow Data Validation, and TensorFlow Transform Analyze models with TensorFlow Model Analysis and ship them with the TFX Model Pusher Component after the ModelValidator TFX Component confirmed that the analysis results are an improvement Deploy models in a variety of environments with TensorFlow Serving, TensorFlow Lite, and TensorFlow.js Learn methods for adding privacy, including differential privacy with TensorFlow Privacy and federated learning with TensorFlow Federated Design model feedback loops to increase your data sets and learn when to update your machine learning models

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 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.