Privacy-Preserving Machine Learning
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 synthe...
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Full Text (via Skillsoft) |
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| Format: | eBook |
| Language: | English |
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New York :
Manning Publications Co. LLC,
2023.
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| Subjects: |
MARC
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| 100 | 1 | |a Chang, J. Morris. | |
| 245 | 1 | 0 | |a Privacy-Preserving Machine Learning |
| 260 | |a New York : |b Manning Publications Co. LLC, |c 2023. | ||
| 300 | |a 1 online resource (264 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 500 | |a Description based upon print version of record. | ||
| 505 | 0 | |a Intro -- inside front cover -- Privacy-Preserving Machine Learning -- Copyright -- contents -- front matter -- preface -- acknowledgments -- about this book -- Who should read this book -- How this book is organized: A road map -- About the code -- liveBook discussion forum -- about the authors -- about the cover illustration -- Part 1 Basics of privacy-preserving machine learning with differential privacy -- 1 Privacy considerations in machine learning -- 1.1 Privacy complications in the AI era -- 1.2 The threat of learning beyond the intended purpose -- 1.2.1 Use of private data on the fly | |
| 505 | 8 | |a 1.2.2 How data is processed inside ML algorithms -- 1.2.3 Why privacy protection in ML is important -- 1.2.4 Regulatory requirements and the utility vs. privacy tradeoff -- 1.3 Threats and attacks for ML systems -- 1.3.1 The problem of private data in the clear -- 1.3.2 Reconstruction attacks -- 1.3.3 Model inversion attacks -- 1.3.4 Membership inference attacks -- 1.3.5 De-anonymization or re-identification attacks -- 1.3.6 Challenges of privacy protection in big data analytics -- 1.4 Securing privacy while learning from data: Privacy-preserving machine learning | |
| 505 | 8 | |a 1.4.1 Use of differential privacy -- 1.4.2 Local differential privacy -- 1.4.3 Privacy-preserving synthetic data generation -- 1.4.4 Privacy-preserving data mining techniques -- 1.4.5 Compressive privacy -- 1.5 How is this book structured? -- Summary -- 2 Differential privacy for machine learning -- 2.1 What is differential privacy? -- 2.1.1 The concept of differential privacy -- 2.1.2 How differential privacy works -- 2.2 Mechanisms of differential privacy -- 2.2.1 Binary mechanism (randomized response) -- 2.2.2 Laplace mechanism -- 2.2.3 Exponential mechanism | |
| 505 | 8 | |a 2.3 Properties of differential privacy -- 2.3.1 Postprocessing property of differential privacy -- 2.3.2 Group privacy property of differential privacy -- 2.3.3 Composition properties of differential privacy -- Summary -- 3 Advanced concepts of differential privacy for machine learning -- 3.1 Applying differential privacy in machine learning -- 3.1.1 Input perturbation -- 3.1.2 Algorithm perturbation -- 3.1.3 Output perturbation -- 3.1.4 Objective perturbation -- 3.2 Differentially private supervised learning algorithms -- 3.2.1 Differentially private naive Bayes classification | |
| 505 | 8 | |a 3.2.2 Differentially private logistic regression -- 3.2.3 Differentially private linear regression -- 3.3 Differentially private unsupervised learning algorithms -- 3.3.1 Differentially private k-means clustering -- 3.4 Case study: Differentially private principal component analysis -- 3.4.1 The privacy of PCA over horizontally partitioned data -- 3.4.2 Designing differentially private PCA over horizontally partitioned data -- 3.4.3 Experimentally evaluating the performance of the protocol -- Summary -- Part 2 Local differential privacy and synthetic data generation | |
| 500 | |a 4 Local differential privacy for machine learning | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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 Authors 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. G. 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. Quotes A detailed treatment of differential privacy, synthetic data generation, and privacy-preserving machine-learning techniques with relevant Python examples. Highly recommended! - Abe Taha, Google A wonderful synthesis of theoretical and practical. This book fills a real need. - Stephen Oates, Allianz The definitive source for creating privacy-respecting machine learning systems. This area in data-rich environments is so important to understand! - Mac Chambers, Roy Hobbs Diamond Enterprises Covers all aspects for data privacy, with good practical examples. - Vidhya Vinay, Streamingo Solutions. | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computer networks |x Security measures. | |
| 650 | 7 | |a Computer networks |x Security measures |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 700 | 1 | |a Zhuang, Di. | |
| 700 | 1 | |a Samaraweera, G. Dumindu. | |
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