Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning is a guide that teaches how to keep ML data anonymous and secure. It demystifies complex privacy-enhancing technologies through real-world use cases and discusses current and future privacy challenges.

Format: Paperback / softback
Length: 300 pages
Publication date: 21 April 2023
Publisher: Manning Publications

Privacy-Preserving Machine Learning is a comprehensive guide to ensuring the anonymity and security of ML data. It delves into the fundamental principles behind various privacy preservation technologies and provides practical insights on how to implement them effectively. Through real-world use cases, such as facial recognition, cloud data storage, and more, complex privacy-enhancing techniques are demystified, making them accessible to practitioners. Alongside technical implementation skills, learners will explore current and emerging privacy challenges in machine learning and learn how to adapt technologies to meet their specific requirements. By the end of this course, participants will possess the knowledge and skills to develop machine learning systems that prioritize user privacy without compromising data quality or model performance.

The growing concern over privacy has led to a significant demand for privacy-preserving techniques in the field of machine learning. Large-scale data breaches, such as the Facebook Cambridge Analytic incident, have raised public awareness about the importance of safeguarding personal information. Machine learning engineers are increasingly seeking solutions that allow users to maintain their privacy while still benefiting from the capabilities of machine learning models.

Privacy-Preserving Machine Learning addresses this need by providing a comprehensive framework for protecting ML data. It covers the core principles behind different privacy preservation technologies, such as differential privacy, k-anonymization, and encryption. The course also emphasizes the practical aspects of implementing these technologies, including techniques for data anonymization, model training, and evaluation.

Through real-world use cases and case studies, learners will gain hands-on experience with implementing privacy-preserving techniques in various ML applications. They will explore how to apply these techniques to scenarios such as facial recognition, medical imaging, and financial analysis, ensuring that user privacy is protected without compromising the performance of the models.

In addition to technical skills, Privacy-Preserving Machine Learning also emphasizes the ethical and legal considerations associated with privacy preservation. Learners will learn about the relevant regulations and guidelines, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), and how to comply with them while developing ML systems.

By the end of the course, participants will have a deep understanding of privacy-preserving machine learning and its applications in various industries. They will be equipped with the skills and knowledge to develop privacy-sensitive machine learning systems that meet the growing demand for data protection and privacy.

In conclusion, Privacy-Preserving Machine Learning is a crucial course for anyone working with machine learning data. It provides a comprehensive framework for ensuring the anonymity and security of ML data, while also emphasizing the ethical and legal considerations associated with privacy preservation. With its practical approach and real-world use cases, the course is designed to help practitioners develop privacy-sensitive machine learning systems that meet the growing demand for data protection and privacy.

Weight: 636g
Dimension: 187 x 236 x 21 (mm)
ISBN-13: 9781617298042