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.
