Privacy Enhanced Federated Learning Utilizing Differential Privacy and Interplanetary File System

Hyowon Kim, Inshil Doh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

As the Internet of Things (IoT) grows exponentially, it is becoming deeply embedded in our daily lives. As the quantity and quality of data produced by devices have also gradually increased, there have been increasing attempts to use these useful IoT big data for various applications and to combine IoT with machine learning and deep learning to process a large amount of useful data. However, in the centralized deep learning method, privacy issues have been raised because the server can use personal data collected from the user's IoT. Due to this reason, Federated Learning (FL) method that can protect users' personal data while doing machine learning has been studied. However, current FL also has the possibility of data poisoning attacks and other problems. Therefore, this work, by suggesting distributed FL framework combined with Interplanetary File System and Differential Privacy, proposes a method that allows users to participate in FL safely and efficiently. Through this method, participants share some parts of data, and these data are collected by specific nodes. These data are combined to make a new dataset of FL network for defending against data poisoning attack and vouch for training's accuracy. Also, an aggregation mechanism is proposed to suppress the effect of a malicious node poisoning attack. Finally, this framework is tested in python environment. With this method, one can freely open a project and anyone can join in with distributed condition, even when he or she has no enough dataset for learning but computing capability, vice versa. If a malicious node tries to interrupt the learning with poisoned dataset, aggregation mechanism and combined validation set from the network's nodes will suppress the bad effect. We have tested through python and open-source code to verify the efficiency and privacy.

Original languageEnglish
Title of host publication37th International Conference on Information Networking, ICOIN 2023
PublisherIEEE Computer Society
Pages312-317
Number of pages6
ISBN (Electronic)9781665462686
DOIs
StatePublished - 2023
Event37th International Conference on Information Networking, ICOIN 2023 - Bangkok, Thailand
Duration: 11 Jan 202314 Jan 2023

Publication series

NameInternational Conference on Information Networking
Volume2023-January
ISSN (Print)1976-7684

Conference

Conference37th International Conference on Information Networking, ICOIN 2023
Country/TerritoryThailand
CityBangkok
Period11/01/2314/01/23

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government [Ministry of Science and ICT (MSIT)] under Grant NRF-2020R1A2C1006497

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government [Ministry of Science and ICT (MSIT)] under rG ant NRF-2020R1A2C1006.749 Inshil Doh is the corresponding author.

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Data Poisoning Attack
  • Differential Privacy
  • Federated Learning
  • Interplanetary File System
  • IoT

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