ElZemity, Adel, Arief, Budi (2024) Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review. In: IEEE Congress on Cybermatics: 2024 IEEE International Conferences on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical and Social Computing; IEEE Smart Data. . pp. 331-338. IEEE ISBN 979-8-3503-5164-4. E-ISBN 979-8-3503-5163-7. (doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00072) (KAR id:107769)
PDF
Author's Accepted Manuscript
Language: English DOI for this version: 10.22024/UniKent/01.02.107769.3445383
This work is licensed under a Creative Commons Attribution 4.0 International License.
|
|
Download this file (PDF/536kB) |
Preview |
Request a format suitable for use with assistive technology e.g. a screenreader | |
Official URL: https://doi.org/10.1109/ithings-greencom-cpscom-sm... |
Abstract
Federated Learning (FL) in the Internet of Things (IoT) environments can enhance machine learning by utilising decentralised data, but at the same time, it might introduce significant privacy and security concerns due to the constrained nature of IoT devices. This represents a research challenge that we aim to address in this paper. We systematically analysed recent literature to identify privacy threats in FL within IoT environments, and evaluate the defensive measures that can be employed to mitigate these threats. Using a Systematic Literature Review (SLR) approach, we searched five publication databases (Scopus, IEEE Xplore, Wiley, ACM, and Science Direct), collating relevant papers published between 2017 and April 2024, a period which spans from the introduction of FL until now. Guided by the PRISMA protocol, we selected 49 papers to focus our systematic review on. We analysed these papers, paying special attention to the privacy threats and defensive measures – specifically within the context of IoT – using inclusion and exclusion criteria tailored to highlight recent advances and critical insights. We identified various privacy threats, including inference attacks, poisoning attacks, and eavesdropping, along with defensive measures such as Differential Privacy and Secure Multi-Party Computation. These defences were evaluated for their effectiveness in protecting privacy without compromising the functional integrity of FL in IoT settings. Our review underscores the necessity for robust and efficient privacy-preserving strategies tailored for IoT environments. Notably, there is a need for strategies against replay, evasion, and model stealing attacks. Exploring lightweight defensive measures and emerging technologies such as blockchain may help improve the privacy of FL in IoT, leading to the creation of FL models that can operate under variable network conditions.
Item Type: | Conference or workshop item (Proceeding) |
---|---|
DOI/Identification number: | 10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00072 |
Additional information: | For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission |
Uncontrolled keywords: | Privacy, Differential privacy, Social computing, Computational modeling, Multi-party computation, Robustness, Blockchains, Internet of Things, Time factors, Security |
Subjects: | Q Science > QA Mathematics (inc Computing science) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Funders: | Engineering and Physical Sciences Research Council (https://ror.org/0439y7842) |
SWORD Depositor: | JISC Publications Router |
Depositing User: | JISC Publications Router |
Date Deposited: | 22 Nov 2024 14:46 UTC |
Last Modified: | 25 Nov 2024 12:56 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/107769 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):