Secure collaborative deep learning against GAN attacks in the internet of things
Deep learning makes the Internet-of-Things (IoT) devices more attractive, and in turn, IoT facilitates the resolution of the contradiction between data collection and privacy concerns. IoT devices with small-scale computing power can contribute to model training without sharing data in collaborative...
Saved in:
Main Authors: | CHEN, Zhenzhu, FU, Anmin, ZHANG, Yinghui, LIU, Zhe, ZENG, Fanjian, DENG, Robert H. |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2021
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/6681 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
MP-CLF: An effective model-preserving collaborative deep learning framework for mitigating data leakage under the GAN
by: CHEN, Zhenzhu, et al.
Published: (2023) -
Secure smart health with privacy-aware aggregate authentication and access control in Internet of Things
by: ZHANG, Yinghui, et al.
Published: (2018) -
Secure and verifiable outsourced data dimension reduction on dynamic data
by: CHEN, Zhenzhu, et al.
Published: (2021) -
Realisation of security for the internet of things
by: Aung Naing Oo
Published: (2016) -
Security in internet of things design
by: Ricardy, Bob
Published: (2016)