Book descriptionKeep 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.
- 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.
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
Table of contents
- inside front cover
- Privacy-Preserving Machine Learning
- front matter
- 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
- 1.3 Threats and attacks for ML systems
- 1.4 Securing privacy while learning from data: Privacy-preserving machine learning
- 1.5 How is this book structured?
2 Differential privacy for machine learning
- 2.1 What is differential privacy?
- 2.2 Mechanisms of differential privacy
- 2.3 Properties of differential privacy
3 Advanced concepts of differential privacy for machine learning
- 3.1 Applying differential privacy in machine learning
- 3.2 Differentially private supervised learning algorithms
- 3.3 Differentially private unsupervised learning algorithms
- 3.4 Case study: Differentially private principal component analysis
- Part 2 Local differential privacy and synthetic data generation
- 4 Local differential privacy for machine learning
5 Advanced LDP mechanisms for machine learning
- 5.1 A quick recap of local differential privacy
- 5.2 Advanced LDP mechanisms
- 5.3 A case study implementing LDP naive Bayes classification
6 Privacy-preserving synthetic data generation
- 6.1 Overview of synthetic data generation
- 6.2 Assuring privacy via data anonymization
- 6.3 DP for privacy-preserving synthetic data generation
- 6.4 Case study on private synthetic data release via feature-level micro-aggregation
- Part 3 Building privacy-assured machine learning applications
7 Privacy-preserving data mining techniques
- 7.1 The importance of privacy preservation in data mining and management
- 7.2 Privacy protection in data processing and mining
- 7.3 Protecting privacy by modifying the input
- 7.4 Protecting privacy when publishing data
8 Privacy-preserving data management and operations
- 8.1 A quick recap of privacy protection in data processing and mining
- 8.2 Privacy protection beyond k-anonymity
- 8.3 Protecting privacy by modifying the data mining output
8.4 Privacy protection in data management systems
- 8.4.1 Database security and privacy: Threats and vulnerabilities
- 8.4.2 How likely is a modern database system to leak private information?
- 8.4.3 Attacks on database systems
- 8.4.4 Privacy-preserving techniques in statistical database systems
- 8.4.5 What to consider when designing a customizable privacy-preserving database system
9 Compressive privacy for machine learning
- 9.1 Introducing compressive privacy
- 9.2 The mechanisms of compressive privacy
- 9.3 Using compressive privacy for ML applications
9.4 Case study: Privacy-preserving PCA and DCA on horizontally partitioned data
- 9.4.1 Achieving privacy preservation on horizontally partitioned data
- 9.4.2 Recapping dimensionality reduction approaches
- 9.4.3 Using additive homomorphic encryption
- 9.4.4 Overview of the proposed approach
- 9.4.5 How privacy-preserving computation works
- 9.4.6 Evaluating the efficiency and accuracy of the privacy-preserving PCA and DCA
10 Putting it all together: Designing a privacy-enhanced platform (DataHub)
- 10.1 The significance of a research data protection and sharing platform
- 10.2 Understanding the research collaboration workspace
- 10.3 Integrating privacy and security technologies into DataHub
Appendix A. More details about differential privacy
- A.1 The formal definition of differential privacy
- A.2 Other differential privacy mechanisms
- A.3 Formal definitions of composition properties of DP
- inside back cover
- Title: Privacy-Preserving Machine Learning
- Release date: May 2023
- Publisher(s): Manning Publications
- ISBN: 9781617298042
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