Privacy-Preserving Machine Learning - Tong Li,Ping Li,Zheli Liu,Jin Li,Xiaofeng Chen
-30% with code BOOKS
Shipping in 12-18 days
30-day return policy
This book provides a thorough overview of the evolution of privacy-preserving machine learning schemes over the last ten years, after discussing the importance of privacy-preserving techniques. In response to the diversity of Internet services, data services based on machine learning are now available for various applications, including risk assessment and image recognition. In light of open access to datas ... Full description
You May Also Like
TOP
If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All
Eliezer Yudkowsky, Nate Soares
-30% with code BOOKS
In stock at our supplier
TOP
The 21st Century Brain: Cutting edge neuroscience to help us navigate the future
-30% with code BOOKS
In stock at our supplier
TOP
Gödel, Escher, Bach: An Eternal Golden Braid
-30% with code BOOKS
In stock at our supplier
TOP
AI Engineering: Building Applications with Foundation Models
-30% with code BOOKS
In stock at our supplier
TOP
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
-30% with code BOOKS
In stock at our supplier
TOP
Python Crash Course: A Hands-On, Project-Based Introduction to Programming
-30% with code BOOKS
In stock at our supplier
TOP
How To Think About AI: A Guide For The Perplexed
-30% with code BOOKS
In stock at our supplier
TOP
The Rust Programming Language
Steve Klabnik, Carol Nichols, Chris Krycho
-30% with code BOOKS
In stock at our supplier
TOP
How to Talk to AI: (And How Not To)
-30% with code BOOKS
In stock at our supplier
TOP
Why Machines Learn: The Elegant Math Behind Modern AI
-30% with code BOOKS
In stock at our supplier
TOP
The Pragmatic Programmer: journey to mastery, 20th Anniversary Edition, 2/e: your journey to mastery, 20th Anniversary Edition
-30% with code BOOKS
In stock at our supplier
TOP
Hackers. 25th Anniversary Edition: Heroes of the Computer Revolution
-30% with code BOOKS
In stock at our supplier
TOP
The Art of Game Design: A Book of Lenses
-30% with code BOOKS
In stock at our supplier
TOP
How Linux Works: What Every Superuser Should Know
-30% with code BOOKS
In stock at our supplier
TOP
Hands-On Large Language Models: Language Understanding and Generation
Maarten Grootendorst, Jay Alammar
-30% with code BOOKS
In stock at our supplier
TOP
Fundamentals of Software Architecture: A Modern Engineering Approach
-30% with code BOOKS
In stock at our supplier
TOP
Slanted 47 Digital Tools: Slanted Magazine
-30% with code BOOKS
In stock at our supplier
TOP
Linux Basics for Hackers, 2nd Edition: Getting Started with Networking, Scripting, and Security in Kali
-30% with code BOOKS
In stock at our supplier
TOP
Deep Learning: Foundations and Concepts
Christopher M. Bishop, Hugh Bishop
-30% with code BOOKS
In stock at our supplier
Description
This book provides a thorough overview of the evolution of privacy-preserving machine learning schemes over the last ten years, after discussing the importance of privacy-preserving techniques. In response to the diversity of Internet services, data services based on machine learning are now available for various applications, including risk assessment and image recognition. In light of open access to datasets and not fully trusted environments, machine learning-based applications face enormous security and privacy risks. In turn, it presents studies conducted to address privacy issues and a series of proposed solutions for ensuring privacy protection in machine learning tasks involving multiple parties. In closing, the book reviews state-of-the-art privacy-preserving techniques and examines the security threats they face.
More Information
| Author | Tong Li, Ping Li, Zheli Liu, Jin Li, Xiaofeng Chen |
|---|---|
| Publisher | Springer Nature Singapore |
| Series | SpringerBriefs on Cyber Security Systems and Networks |
| Release year | 2022 |
| Cover type | Softcover |
| EAN | 9789811691386 |