Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning is a practical guide to keeping ML data anonymous and secure. You''ll learn the core principles behind different privacy preservation technologies, and how to put theory into practice for your own machine learning.

Complex privacy-enhancing technologies are demystified through real world use cases forfacial recognition, cloud data storage, and more. Alongside skills for technical implementation, you''ll learn about current and future machine learning privacy challenges and how to adapt technologies to your specific needs. By the time you''re done, you''ll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance.

Large-scale scandals such as the Facebook Cambridge Analytic a data breach have made many users wary of sharing sensitive and personal information. Demand has surged among machine learning engineers for privacy-preserving techniques that can keep users private details secure without adversely affecting the performance of models.

Keep sensitive user data safe and secure, without sacrificing theaccuracy of your machine learning models.

In Privacy Preserving Machine Learning, you will learn:

• Differential privacy techniques and their application insupervised learning
• Privacy for frequency or mean estimation, Naive Bayes classifier,and deep learning
• Designing and applying compressive privacy for machine learning
• Privacy-preserving synthetic data generation approaches
• Privacy-enhancing technologies for data mining and database applications

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 and seniorities will benefit from incorporating these privacy-preserving practices into their model development.