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

Srinivasa Rao Aravilli

€33.99

5 (8 Ratings)

Paperback May 2024 402 pages 1st Edition

Srinivasa Rao Aravilli

€33.99

5 (8 Ratings)

Paperback May 2024 402 pages 1st Edition

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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

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Amazon verified reviews

Chandra Jun 09, 2024

The book on "Privacy-Preserving Machine Learning" progresses from basic principles to advanced techniques, ensuring a solid foundation for readers at all levels. It includes practical applications through case studies and examples, illustrating real-world challenges and solutions. This comprehensive approach provides both theoretical knowledge and practical insights, making it a valuable resource for understanding and implementing privacy-preserving techniques in machine learning.The book begins by exploring the fundamentals of Data Privacy, differentiating between sensitive and personal sensitive data, and underscoring the importance of data privacy regulations, laws and its data subject rights. Very details on the "Privacy by design”(PbD) framework and its foundation principles as a critical approach for ensuring privacy throughout the data life cycle with detailed example of application/platform on how each PbD principle can implemented. Discusses the repercussions of Privacy Breaches through notable cases with different types of application includes web application, AI/ML applications, etc. The introduction of the frameworks (LINDDUN, STRIDE, PLOT4AI) aids in understanding privacy threat modeling.The subsequent section delves into Machine Learning, covering ML types like Supervised, Unsupervised, and Reinforcement learning with detailed examples. And preparing the ML models includes model persistence with different formats and using the model for inference. Next goes into ML Phases and sub-phases. After detailed explanation of Machine learning concepts, it discusses the challenges in persisting ML models and the Privacy threats/attacks includes Black-box and White-box type attack with each ML phase in details with examples, including model inversion and training data extraction attacks. By providing a detailed examination of privacy needs and threats in machine learning, complemented with examples of attacks using open-source frameworks, the book equips readers with a comprehensive understanding and significance of privacy-preserving techniques in safeguarding data in machine learning applications.The book introduces Privacy Preserving Data analysis, focusing on Privacy Enhanced Technologies and Differential Privacy, offering a solid foundation for implementing privacy measures in data analysis and machine learning. Detailed coverage on the different Privacy Preserving techniques includes Data anonymization i.e. K-anonymity and Data aggregation with examples and code. It discusses the prevention of reconstruction attacks on SQL through the Open Diffix framework and delves into the nuances of differential privacy, including privacy loss, budgets, and mechanisms. Further exploration into differential privacy covers algorithms like Laplace, Gaussian, and others, addressing the limitations of differential privacy. A practical section on developing applications with differential privacy showcases the implementation in fraud detection using open-source ML and DL frameworks (PyDP, PipelineDP, tmlt-analytics, PySpark,diffprivlib, PyTorch, and Opacus), alongside real-world applications. This comprehensive coverage ensures a deep understanding of differential privacy’s principles, applications, and challenges.The section Hands-on Federated Learning (FL) underscores its significance in addressing privacy concerns by allowing model training without centralizing data, exploring the differences between IID and non-IID datasets crucial for FL implementation. It introduces FL techniques such as FedAvg, FedYogi, FedSGD, and showcases the use of the Flower framework for a financial domain use case. Further, it reviews FL benchmarks and datasets, guiding the selection process for FL projects based on data characteristics and evaluation criteria. It also highlights the latest FL research, methodologies, challenges, and the role of startups in advancing FL technology, providing a comprehensive view of the current and future opportunities in federated learning.The book delves into privacy-enhancing techniques like homomorphic encryption (HE) and secure multiparty computation (SMC), detailing their principles, mathematical foundations, and application in machine learning to enable computations on encrypted data. It also introduces zero-knowledge proofs (ZKP) for verifying knowledge without revealing the information itself. The concept of confidential computing is explored, emphasizing the importance of protecting data in memory using trusted execution environments (TEEs) and source code attestation to mitigate insider threats. It discusses the privacy vulnerabilities of large language models (LLMs) and strategies for preserving privacy when utilizing these models, including defenses against various privacy attacks. Lastly, it evaluates the support for secure enclaves across major cloud service providers, aiding in the decision-making process for deploying secure enclave-reliant applications. This comprehensive overview highlights the importance of privacy-preserving techniques in safeguarding sensitive data across different stages and platforms, ensuring data utility while maintaining privacy.

Amazon Verified review

bharat Jun 09, 2024

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.

satya pallapothu Jul 10, 2024

A well written book, this is a must read if you need comprehensive understanding of Privacy-Preserving machine learning. Provides real-world use cases to develop and deploy privacy-preserving ML pipelines using open-source frameworks. A great resource for all personnel involved in ML projects.

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.

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.

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

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