Privacy-Preserving Machine Learning (häftad, eng)

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.