Financial Privacy Computing Applications with Distributed Machine Learning

DOI

10.2991/978-94-6463-304-7_58How to use a DOI?

Keywords

Federated learning; private computing; distributed machine learning; big data

Abstract

With the further development of big data applications, data privacy and security have attracted great attention from all countries in the world. There are nearly 100 countries and regions in the world that have laws related to data security protection. Through legislation, data users have more control to control their personal data. At the same time, most of the industry data present the phenomenon of data Island, how to carry out cross-organizational data cooperation under the premise of meeting user privacy protection, data security and government regulations is a major problem, and federated machine learning will become the key technology to solve this industry problem.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Guanhao Feng
PY  - 2023
DA  - 2023/12/04
TI  - Financial Privacy Computing Applications with Distributed Machine Learning
BT  - Proceedings of the 3rd International Conference on Digital Economy and Computer Application (DECA 2023)
PB  - Atlantis Press
SP  - 555
EP  - 566
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-304-7_58
DO  - 10.2991/978-94-6463-304-7_58
ID  - Feng2023
ER  -