Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

A comprehensive guide that explores data privacy threats and attacks across different ML phases. It delves into real-world cases from Uber and Apple to illustrate differential privacy and data security enhancements. The book covers IID and non-IID data sets, data categories, and the use of open-source tools for federated learning. It also provides insights into FL algorithms, benchmarks, secure multiparty computation with PSI for large data, and confidential computation to protect against data-in-memory attacks. A must-read for anyone looking to secure their ML projects.

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.

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.

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.

This book serves as an essential guide for anyone aiming to implement machine learning techniques in the area of security and privacy.The author exhibits a deep understanding of the security and privacy field coupled with a deep understanding of how these aspects need to be integrated with machine learning. He not only show a keen comprehension of the complex landscape of security and privacy, but also articulate the nuances of merging these with the dynamic field of machine learning, underscoring the necessity for strategic and mindful integration.The book lays the groundwork with an introduction to Data Privacy, making a distinction between confidential and personally confidential data, and emphasising the significance of data privacy regulations, laws and the rights of data subjects. It provides a thorough explanation of the "Privacy by design"(PbD) framework and its foundational principles, illustrating how each principle can be applied using detailed examples of applications/platforms. It also addresses the consequences of Privacy Breaches via notable case studies across various application types, including web applications and AI/ML applications. The book introduces frameworks such as LINDDUN, STRIDE, and PLOT4AI to enhance understanding of privacy threat modeling.The book talks about how to maintain data privacy during analysis, focusing on Differential Privacy. This provides a strong foundation for incorporating privacy measures when conducting data analysis and machine learning. The book explores various methods to ensure data privacy, such as Data Anonymisation (for example, K-anonymity) and Data Aggregation, supported by examples and code for better understanding. It also explains how to prevent reconstruction attacks on SQL using the Open Diffix framework and delves into the complexities of differential privacy, including privacy loss, budgets, and methods. The book further elaborates on differential privacy algorithms like Laplace, Gaussian, among others, and underscores the limitations of differential privacy. It includes a practical section that demonstrates the application of differential privacy in fraud detection using Machine Learning and Deep Learning frameworks (like PyDP, PipelineDP, tmlt-analytics, PySpark, diffprivlib, PyTorch, and Opacus), accompanied by real-world examples.The section on Federated Learning (FL) explains its importance in dealing with privacy issues by allowing models to be trained without needing to centralise data. It talks about the difference between IID and non-IID datasets, which are very important for implementing FL. It introduces FL methods like FedAvg, FedYogi, FedSGD, and shows how the Flower framework can be used for a case in the finance sector. It also looks at FL benchmarks and datasets, offering advice on choosing FL projects based on the type of data and how to evaluate them. Moreover, it talks about the latest research in FL, methodologies, challenges, and how startups are helping to advance FL technology. This gives a full overview of the current and future opportunities in federated learning.The book talks about ways to enhance privacy like homomorphic encryption (HE) and secure multiparty computation (SMC). It goes into detail about how they work, their math foundations, and how they are used in machine learning to do calculations on encrypted data. It also introduces zero-knowledge proofs (ZKP), which can check knowledge without giving away the information itself. It looks at the idea of confidential computing, highlighting the need to keep data in memory safe using trusted execution environments (TEEs) and source code attestation to stop insider threats. It talks about the privacy weak points of large language models (LLMs) and how to keep privacy when using these models, including ways to defend against different privacy attacks. Lastly, it checks the support for secure enclaves across major cloud service providers, helping in deciding how to deploy applications that rely on secure enclaves. This full overview highlights the importance of techniques that preserve privacy in keeping sensitive data safe across different stages and platforms, making sure data is useful while keeping it private.