Privacy Preserving Machine Learning

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Privacy-Preserving Machine Learning

J. Morris Chang,Di Zhuang,G. Dumindu Samaraweera
Privacy Preserving Machine Learning Book PDF

Author : J. Morris Chang,Di Zhuang,G. Dumindu Samaraweera
Publisher : Simon and Schuster
Page : 334 pages
File Size : 42,5 Mb
Release : 2023-05-02
Category : Computers
ISBN : 9781617298042

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Privacy-Preserving Machine Learning by J. Morris Chang,Di Zhuang,G. Dumindu Samaraweera Pdf

Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation Privacy-enhancing technologies for data mining and database applications Compressive privacy for machine learning Privacy-Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You’ll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. By the time you’re done reading, you’ll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning applications need massive amounts of data. It’s up to you to keep the sensitive information in those data sets private and secure. Privacy preservation happens at every point in the ML process, from data collection and ingestion to model development and deployment. This practical book teaches you the skills you’ll need to secure your data pipelines end to end. About the Book Privacy-Preserving Machine Learning explores privacy preservation techniques through real-world use cases in facial recognition, cloud data storage, and more. You’ll learn about practical implementations you can deploy now, future privacy challenges, and how to adapt existing technologies to your needs. Your new skills build towards a complete security data platform project you’ll develop in the final chapter. What’s Inside Differential and compressive privacy techniques Privacy for frequency or mean estimation, naive Bayes classifier, and deep learning Privacy-preserving synthetic data generation Enhanced privacy for data mining and database applications About the Reader For machine learning engineers and developers. Examples in Python and Java. About the Author J. Morris Chang is a professor at the University of South Florida. His research projects have been funded by DARPA and the DoD. Di Zhuang is a security engineer at Snap Inc. Dumindu Samaraweera is an assistant research professor at the University of South Florida. The technical editor for this book, Wilko Henecka, is a senior software engineer at Ambiata where he builds privacy-preserving software. Table of Contents PART 1 - BASICS OF PRIVACY-PRESERVING MACHINE LEARNING WITH DIFFERENTIAL PRIVACY 1 Privacy considerations in machine learning 2 Differential privacy for machine learning 3 Advanced concepts of differential privacy for machine learning PART 2 - LOCAL DIFFERENTIAL PRIVACY AND SYNTHETIC DATA GENERATION 4 Local differential privacy for machine learning 5 Advanced LDP mechanisms for machine learning 6 Privacy-preserving synthetic data generation PART 3 - BUILDING PRIVACY-ASSURED MACHINE LEARNING APPLICATIONS 7 Privacy-preserving data mining techniques 8 Privacy-preserving data management and operations 9 Compressive privacy for machine learning 10 Putting it all together: Designing a privacy-enhanced platform (DataHub)

Privacy-Preserving Machine Learning

Srinivasa Rao Aravilli
Privacy Preserving Machine Learning Book PDF

Author : Srinivasa Rao Aravilli
Publisher : Packt Publishing Ltd
Page : 402 pages
File Size : 44,8 Mb
Release : 2024-05-24
Category : Computers
ISBN : 9781800564220

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Privacy-Preserving Machine Learning by Srinivasa Rao Aravilli Pdf

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 DescriptionPrivacy regulations are evolving each year and compliance with privacy regulations is mandatory for every enterprise. Machine learning engineers are required to not only analyze large amounts of data to gain crucial insights, but also comply with privacy regulations to protect sensitive data. This may seem quite challenging considering the large volume of data involved and lack of in-depth expertise in privacy-preserving machine learning. This book delves into data privacy, machine learning privacy threats, and real-world cases of privacy-preserving machine learning, as well as open-source frameworks for implementation. You’ll be guided through developing anti-money laundering solutions via federated learning and differential privacy. Dedicated sections also address data in-memory attacks and strategies for safeguarding data and ML models. The book concludes by discussing the necessity of confidential computation, privacy-preserving machine learning benchmarks, and cutting-edge research. By the end of this machine learning book, you’ll be well-versed in privacy-preserving machine learning and know how to effectively protect data from threats and attacks in the real world.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 book is for data scientists, machine learning engineers, and privacy engineers who have working knowledge of mathematics as well as basic knowledge in any one of the ML frameworks (TensorFlow, PyTorch, or scikit-learn).

Privacy-Preserving Deep Learning

Kwangjo Kim,Harry Chandra Tanuwidjaja
Privacy Preserving Deep Learning Book PDF

Author : Kwangjo Kim,Harry Chandra Tanuwidjaja
Publisher : Springer Nature
Page : 81 pages
File Size : 45,6 Mb
Release : 2021-07-22
Category : Computers
ISBN : 9789811637643

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Privacy-Preserving Deep Learning by Kwangjo Kim,Harry Chandra Tanuwidjaja Pdf

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

Jin Li,Ping Li,Zheli Liu,Xiaofeng Chen,Tong Li
Privacy Preserving Machine Learning Book PDF

Author : Jin Li,Ping Li,Zheli Liu,Xiaofeng Chen,Tong Li
Publisher : Springer Nature
Page : 95 pages
File Size : 44,8 Mb
Release : 2022-03-14
Category : Computers
ISBN : 9789811691393

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Privacy-Preserving Machine Learning by Jin Li,Ping Li,Zheli Liu,Xiaofeng Chen,Tong Li Pdf

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.

Pattern Recognition and Machine Learning

Christopher M. Bishop
Pattern Recognition and Machine Learning Book PDF

Author : Christopher M. Bishop
Publisher : Springer
Page : 0 pages
File Size : 47,5 Mb
Release : 2016-08-23
Category : Computers
ISBN : 1493938436

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Pattern Recognition and Machine Learning by Christopher M. Bishop Pdf

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Grokking Deep Learning

Andrew W. Trask
Grokking Deep Learning Book PDF

Author : Andrew W. Trask
Publisher : Simon and Schuster
Page : 475 pages
File Size : 51,5 Mb
Release : 2019-01-23
Category : Computers
ISBN : 9781638357209

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Grokking Deep Learning by Andrew W. Trask Pdf

Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide

Federated Learning Systems

Muhammad Habib ur Rehman,Mohamed Medhat Gaber
Federated Learning Systems Book PDF

Author : Muhammad Habib ur Rehman,Mohamed Medhat Gaber
Publisher : Springer Nature
Page : 207 pages
File Size : 43,7 Mb
Release : 2021-06-11
Category : Technology & Engineering
ISBN : 9783030706043

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Federated Learning Systems by Muhammad Habib ur Rehman,Mohamed Medhat Gaber Pdf

This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.

Research Anthology on Privatizing and Securing Data

Management Association, Information Resources
Research Anthology on Privatizing and Securing Data Book PDF

Author : Management Association, Information Resources
Publisher : IGI Global
Page : 2188 pages
File Size : 50,6 Mb
Release : 2021-04-23
Category : Computers
ISBN : 9781799889557

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Research Anthology on Privatizing and Securing Data by Management Association, Information Resources Pdf

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.

Federated Learning

Qiang Qiang Yang,Yang Yang Liu,Yong Yong Cheng,Yan Yan Kang,Tianjian Tianjian Chen,Han Han Yu
Federated Learning Book PDF

Author : Qiang Qiang Yang,Yang Yang Liu,Yong Yong Cheng,Yan Yan Kang,Tianjian Tianjian Chen,Han Han Yu
Publisher : Springer Nature
Page : 189 pages
File Size : 50,5 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015854

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Federated Learning by Qiang Qiang Yang,Yang Yang Liu,Yong Yong Cheng,Yan Yan Kang,Tianjian Tianjian Chen,Han Han Yu Pdf

How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

Privacy-Preserving Machine Learning

J. Morris Chang,Di Zhuang,G. Dumindu Samaraweera
Privacy Preserving Machine Learning Book PDF

Author : J. Morris Chang,Di Zhuang,G. Dumindu Samaraweera
Publisher : Simon and Schuster
Page : 334 pages
File Size : 50,9 Mb
Release : 2023-05-23
Category : Computers
ISBN : 9781638352754

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Privacy-Preserving Machine Learning by J. Morris Chang,Di Zhuang,G. Dumindu Samaraweera Pdf

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)

Privacy-Preserving Machine Learning for Speech Processing

Manas A. Pathak
Privacy Preserving Machine Learning for Speech Processing Book PDF

Author : Manas A. Pathak
Publisher : Springer Science & Business Media
Page : 145 pages
File Size : 54,9 Mb
Release : 2012-10-26
Category : Technology & Engineering
ISBN : 9781461446392

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Privacy-Preserving Machine Learning for Speech Processing by Manas A. Pathak Pdf

This thesis discusses the privacy issues in speech-based applications such as biometric authentication, surveillance, and external speech processing services. Author Manas A. Pathak presents solutions for privacy-preserving speech processing applications such as speaker verification, speaker identification and speech recognition. The author also introduces some of the tools from cryptography and machine learning and current techniques for improving the efficiency and scalability of the presented solutions. Experiments with prototype implementations of the solutions for execution time and accuracy on standardized speech datasets are also included in the text. Using the framework proposed may now make it possible for a surveillance agency to listen for a known terrorist without being able to hear conversation from non-targeted, innocent civilians.

The Algorithmic Foundations of Differential Privacy

Cynthia Dwork,Aaron Roth
The Algorithmic Foundations of Differential Privacy Book PDF

Author : Cynthia Dwork,Aaron Roth
Publisher : Unknown
Page : 286 pages
File Size : 48,7 Mb
Release : 2014
Category : Computers
ISBN : 1601988184

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The Algorithmic Foundations of Differential Privacy by Cynthia Dwork,Aaron Roth Pdf

The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.

Privacy Preserving Data Mining

Jaideep Vaidya,Christopher W. Clifton,Yu Michael Zhu
Privacy Preserving Data Mining Book PDF

Author : Jaideep Vaidya,Christopher W. Clifton,Yu Michael Zhu
Publisher : Springer Science & Business Media
Page : 124 pages
File Size : 48,6 Mb
Release : 2006-09-28
Category : Computers
ISBN : 9780387294896

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Privacy Preserving Data Mining by Jaideep Vaidya,Christopher W. Clifton,Yu Michael Zhu Pdf

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.

Privacy-Preserving Machine Learning

Srinivasa Rao Aravilli
Privacy Preserving Machine Learning Book PDF

Author : Srinivasa Rao Aravilli
Publisher : Packt Publishing
Page : 0 pages
File Size : 50,5 Mb
Release : 2023-08
Category : Computers
ISBN : 1800564678

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Privacy-Preserving Machine Learning by Srinivasa Rao Aravilli Pdf

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.

Data Science and Security

Samiksha Shukla,Aynur Unal,Joseph Varghese Kureethara,Durgesh Kumar Mishra,Dong Seog Han
Data Science and Security Book PDF

Author : Samiksha Shukla,Aynur Unal,Joseph Varghese Kureethara,Durgesh Kumar Mishra,Dong Seog Han
Publisher : Springer Nature
Page : 503 pages
File Size : 42,9 Mb
Release : 2021-08-26
Category : Technology & Engineering
ISBN : 9789811644863

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Data Science and Security by Samiksha Shukla,Aynur Unal,Joseph Varghese Kureethara,Durgesh Kumar Mishra,Dong Seog Han Pdf

This book presents the best-selected papers presented at the International Conference on Data Science, Computation and Security (IDSCS-2021), organized by the Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus, India, during April 16–17, 2021. The proceeding is targeting the current research works in the areas of data science, data security, data analytics, artificial intelligence, machine learning, computer vision, algorithms design, computer networking, data mining, big data, text mining, knowledge representation, soft computing, and cloud computing.