Privacy Preservation in Distributed Systems

Privacy Preservation in Distributed Systems
Author :
Publisher : Springer Nature
Total Pages : 266
Release :
ISBN-10 : 9783031580130
ISBN-13 : 3031580133
Rating : 4/5 (133 Downloads)

Book Synopsis Privacy Preservation in Distributed Systems by : Guanglin Zhang

Download or read book Privacy Preservation in Distributed Systems written by Guanglin Zhang and published by Springer Nature. This book was released on with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt:


Privacy Preservation in Distributed Systems Related Books

Privacy Preservation in Distributed Systems
Language: en
Pages: 266
Authors: Guanglin Zhang
Categories:
Type: BOOK - Published: - Publisher: Springer Nature

DOWNLOAD EBOOK

Privacy Preservation in Distributed Systems
Language: en
Pages: 0
Authors: Guanglin Zhang
Categories: Technology & Engineering
Type: BOOK - Published: 2024-06-21 - Publisher: Springer

DOWNLOAD EBOOK

This book provides a discussion of privacy in the following three parts: Privacy Issues in Data Aggregation; Privacy Issues in Indoor Localization; and Privacy-
Privacy Preserving Data Mining
Language: en
Pages: 146
Authors: Jaideep Vaidya
Categories: Computers
Type: BOOK - Published: 2005-11-29 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

Privacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in
Stabilization, Safety, and Security of Distributed Systems
Language: en
Pages: 384
Authors: Mohsen Ghaffari
Categories: Computers
Type: BOOK - Published: 2019-11-14 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book constitutes the refereed proceedings of the 21st International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2019, held
Privacy-Preserving Deep Learning
Language: en
Pages: 81
Authors: Kwangjo Kim
Categories: Computers
Type: BOOK - Published: 2021-07-22 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serve