Description: Privacy-preserving Machine Learning, Paperback by Li, Jin; Li, Ping; Liu, Zheli; Chen, Xiaofeng; Li, Tong, ISBN 981169138X, ISBN-13 9789811691386, Like New Used, Free shipping in the US
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, th reviews state-of-the-art privacy-preserving techniques and examines the security threats they face.
Price: 67.37 USD
Location: Jessup, Maryland
End Time: 2024-11-03T15:05:52.000Z
Shipping Cost: 0 USD
Product Images
Item Specifics
Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 14 Days
Refund will be given as: Money Back
Book Title: Privacy-preserving Machine Learning
Number of Pages: VIII, 88 Pages
Language: English
Publication Name: Privacy-Preserving Machine Learning
Publisher: Springer
Subject: Probability & Statistics / General, Intelligence (Ai) & Semantics, Security / Online Safety & Privacy, General
Publication Year: 2022
Item Weight: 5.7 Oz
Type: Textbook
Author: Xiaofeng Chen, Ping Li, Tong Li, Zheli Liu, Jin Li
Item Length: 9.3 in
Subject Area: Mathematics, Computers
Item Width: 6.1 in
Series: Springerbriefs on Cyber Security Systems and Networks Ser.
Format: Trade Paperback
