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Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning

By : Srinivasa Rao Aravilli
5 (8)
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Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning

5 (8)
By: Srinivasa Rao Aravilli

Overview of this book

– In an era of evolving privacy regulations, compliance is mandatory for every enterprise – Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information – This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases – As you progress, you’ll be guided through developing anti-money laundering solutions using federated learning and differential privacy – Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models – You’ll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field – Upon completion, you’ll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks
Table of Contents (17 chapters)
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Free Chapter
1
Part 1: Introduction to Data Privacy and Machine Learning
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4
Part 2: Use Cases of Privacy-Preserving Machine Learning and a Deep Dive into Differential Privacy
8
Part 3: Hands-On Federated Learning
11
Part 4: Homomorphic Encryption, SMC, Confidential Computing, and LLMs

Part 1: Introduction to Data Privacy and Machine Learning

This part provides an introduction to the fundamental concepts of data privacy and the distinction between sensitive data and personal sensitive data, along with the importance of data privacy regulations. The concept of privacy by design is discussed, emphasizing the proactive integration of privacy measures into systems and processes. Additionally, notable privacy breaches in major enterprise companies are examined, highlighting the potential consequences and risks associated with such incidents. This introduction sets the foundation for understanding the significance of data privacy and the need for robust privacy measures. This part also covers privacy threat modeling using the LINDDUN framework in detail.

The second chapter in this part focuses on the different phases of the machine learning pipeline and the privacy threats and attacks that can occur at each stage. We will explore the phases of data collection, data preprocessing, model training, and inference. Within each phase, specific privacy threats and attacks, such as model inversion attacks and training data extraction attacks, are discussed in detail, providing illustrative examples. The importance of protecting training data privacy, input data privacy, model privacy, and inference/output data privacy is emphasized. This part highlights the potential risks and challenges associated with privacy in machine learning, underlining the need for robust privacy preservation techniques throughout the entire process. Exploration of privacy threats and attacks in each phase of the machine learning pipeline sheds light on the challenges of preserving privacy in machine learning systems.

This part has the following chapters:

  • Chapter 1, Introduction to Data Privacy, Privacy Breaches, and Threat Modeling
  • Chapter 2, Machine Learning Phases and Privacy Threats/Attacks in Each Phase
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