Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats Paperback – Import, 24 May 2024

Privacy-Preserving Machine Learning" by Srinivasa Rao Aravilli is a great book that explains how to keep data private while using machine learning. The author makes tough ideas easy to understand and gives useful examples. It's a must-read for anyone who wants to create safe and ethical AI systems. Highly recommended for its clear explanations and importance in today's tech world.

Majority of the plethora of information currently available are either solely devoted to Machine Learning/Deep Learning/AI or particularly focussed on Privacy. The connection between the two is evolving at a very rapid pace and this book does ample justice to the topic by exploring various concepts lying at the synergy of Machine Learning and Privacy - a one stop shop covering issues at the forefront of Privacy driven AI, under its ambit.Of particular interest to me has been the topics covered under Differential Privacy, Synthetic Data Generation and LLM's. Plenty of hands-on as well.Perhaps a more in-depth treatment of AI related to privacy breaking ideas like Reconstruction attacks, Inference attacks etc. could have been explored.However, on a whole, this books serves as a ready reckoner to the diverse topics under Privacy and Machine Learning. There is definitely something in it to the practitioner as well as to the novice, alike.

It is must read for engineers, architects and enthusiasts in Data Privacy and Machine Learning space .Initial chapters in the book set the context on why Privacy By design is an important aspect of software development, different techniques/frameworks to achieve it. Deep dive on LINDDUN framework for privacy threat modeling with samples and hands on labs is very engaging.Author explores the need for privacy preserving machine learning with case studies on important scenarios encountered in technology development and hosting lifecycle. Chapters on different ML types with examples and models on Supervised/Unsupervised and Reinforced Learning are comprehensively articulated .Author also explores Privacy threats in different phases of ML, privacy threat/attack classifications and different Techniques to mitigate each kind off attack.The author holistically covers Privacy in data analysis ,tradeoff between Data utility and Data Privacy. Privacy preserving techniques Data Anonymization Algorithms and effectiveness vs ease comparison with pros and consLater chapters cover Privacy Enhancing Technologies and Privacy Preserving Machine Learning techniques and a deep dive into each of the technique Differential Privacy, Federated Learning,Homomorphic Encryption,SMC ,Confidential Computing, Preserving Privacy in LLMs with algorithms and discuss open source frameworks availability with bench marks and next opportunitiesElaborate samples, case studies, hands on labs make this book interactive.

The book on "Privacy-Preserving Machine Learning" offers an in-depth journey from foundational principles to sophisticated techniques, enriched with real-world case studies. It equips readers with both theoretical understanding and practical skills in implementing privacy-preserving methods in machine learning, making it an essential guide for navigating the complexities of data privacy today.The first section equip readers with a foundational understanding of data privacy and machine learning from a privacy-centric viewpoint, catering to a wide audience from enthusiasts to professionals.The second section of the book receives acclaim for its comprehensive guidance on privacy-preserving data analysis and differential privacy, merging theoretical concepts with practical applications. this section explores differential privacy algorithms and their associated challenges, delivering in-depth knowledge crucial for professionals. this section is dedicated to the practical application of differential privacy in real-world scenarios, providing valuable insights and examples for developers and data scientists.The third section delve deeply into federated learning (FL), underscoring its significance in improving privacy within machine learning. This section covers FL's foundational concepts, techniques, and practical use cases, acting as a valuable resource for professionals looking to incorporate FL into their projects. The following section broadens this exploration, examining FL through benchmarks, recent research, and its application in the start-up environment, shedding light on the potential and hurdles associated with FL.The fourth section of the book delve into sophisticated subjects within data privacy and security, with each part focusing on a distinct facet of the domain. This section demystifies cryptographic methods for data privacy, rendering intricate techniques understandable for a broad readership. This section is dedicated to confidential computing, elaborating on the deployment of Trusted Execution Environments (TEEs) and methods for safeguarding data in memory. This section contemplates the privacy issues associated with developing and utilizing large language models (LLMs), offering a mix of basic and in-depth insights. Collectively, these parts provide a thorough examination of cutting-edge topics in data privacy and security.