This audiobook is narrated by a digital voice.
The exciting world of generative AI offers immense potential for innovation, but its reliance on vast amounts of data raises critical data privacy concerns. This book explores this dynamic landscape, equipping you to understand both the power and the potential pitfalls of generative AI.
Part 1 dives into the core concepts of generative models, from GANs and VAEs to their diverse capabilities. It then explores the data privacy landscape, highlighting the importance of regulations like GDPR and CCPA in the age of AI. You'll gain insights into the specific challenges generative AI poses to data privacy, such as the risk of data leakage through seemingly anonymized training data.
Part 2 delves deeper into these privacy risks. You'll learn how generative models can unintentionally reveal information from their training data and discover techniques to identify and mitigate these leakage risks. The book also explores the potential of synthetic data - artificially generated data that resembles real data but protects privacy. You'll understand the advantages and limitations of synthetic data and explore methods for ensuring privacy-preserving generation techniques.
Part 3 focuses on solutions and building trust. It examines cutting-edge privacy-enhancing techniques for generative AI, such as differential privacy and federated learning. These techniques allow training on data while keeping it encrypted or distributed, safeguarding individual privacy. The book also emphasizes the importance of user control and transparency in generative AI development. You'll explore ways to empower users with control over their data and advocate for clear explanations of how generative models function.
This audiobook is narrated by a digital voice.
The exciting world of generative AI offers immense potential for innovation, but its reliance on vast amounts of data raises critical data privacy concerns. This book explores this dynamic landscape, equipping you to understand both the power and the potential pitfalls of generative AI.
Part 1 dives into the core concepts of generative models, from GANs and VAEs to their diverse capabilities. It then explores the data privacy landscape, highlighting the importance of regulations like GDPR and CCPA in the age of AI. You'll gain insights into the specific challenges generative AI poses to data privacy, such as the risk of data leakage through seemingly anonymized training data.
Part 2 delves deeper into these privacy risks. You'll learn how generative models can unintentionally reveal information from their training data and discover techniques to identify and mitigate these leakage risks. The book also explores the potential of synthetic data - artificially generated data that resembles real data but protects privacy. You'll understand the advantages and limitations of synthetic data and explore methods for ensuring privacy-preserving generation techniques.
Part 3 focuses on solutions and building trust. It examines cutting-edge privacy-enhancing techniques for generative AI, such as differential privacy and federated learning. These techniques allow training on data while keeping it encrypted or distributed, safeguarding individual privacy. The book also emphasizes the importance of user control and transparency in generative AI development. You'll explore ways to empower users with control over their data and advocate for clear explanations of how generative models function.
Generative AI for Data Privacy: Unlocking Innovation, Protecting Rights
Generative AI for Data Privacy: Unlocking Innovation, Protecting Rights
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Product Details
| BN ID: | 2940191193861 |
|---|---|
| Publisher: | Anand Vemula |
| Publication date: | 09/19/2024 |
| Edition description: | Unabridged |
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