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Key benefits

Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches

Develop and deploy privacy-preserving ML pipelines using open-source frameworks

Gain insights into confidential computing and its role in countering memory-based data attacks

Purchase of the print or Kindle book includes a free PDF eBook

Description

– 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

Who is this book for?

– This comprehensive guide is for data scientists, machine learning engineers, and privacy engineers – Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn) – Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques

What you will learn

Study data privacy, threats, and attacks across different machine learning phases

Explore Uber and Apple cases for applying differential privacy and enhancing data security

Discover IID and non-IID data sets as well as data categories

Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks

Understand secure multiparty computation with PSI for large data

Get up to speed with confidential computation and find out how it helps data in memory attacks

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Transformers for Natural Language Processing and Computer Vision

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Total $ 152.97

Steven Fernandes Jun 25, 2024

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.

Amazon Verified review

P N V S Murthy Jun 09, 2024

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.

Amazon Verified review

Amazon Customer Jun 27, 2024

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.

Amazon Verified review

Tanya Aug 07, 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.

Amazon Verified review

Rajesh Kumar M Aug 16, 2024

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.

Amazon Verified review

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

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

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