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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
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

Key company products related to FL

As we have seen FL is a rapidly growing field that has gained significant attention from start-up companies. Here is a summary of some of the companies that are working or providing FL products:

  • DynamoFL: DynamoFL is built by privacy and machine learning experts from MIT (Massachusetts Institute of Technology) and Harvard, who built leading FL solutions at Google AI and privacy-enhanced technologies at Microsoft. As per its website, the current large language models (LLMs) are not private, but the LLMs from DynamoFL are. They provide personalized FL, which is another key area of research. LLMs are covered in Chapter 10 in the Privacy-preserved generative AI section.
  • NVIDIA FLARE: NVIDIA Federated Learning Application Runtime Environment (FLARE) is an SDK for FL that is open sourced by NVIDIA. FLARE supports various FL algorithms (FedAvg, FedProx, FedOpt, etc.), neural networks, and ML algorithms. It supports differential privacy and homomorphic...

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