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Perturbation-enabled Deep Federated Learning for Preserving Internet of Things-based Social Networks

Authors:

Benjamin Turnbull,

Imran RazzakAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 18, Issue 2s

Article No.: 120, Pages 1 - 19

https://doi.org/10.1145/3537899

Published: 06 October 2022 Publication History

Abstract

Federated Learning (FL), as an emerging form of distributed machine learning (ML), can protect participants’ private data from being substantially disclosed to cyber adversaries. It has potential uses in many large-scale, data-rich environments, such as the Internet of Things (IoT), Industrial IoT, Social Media (SM), and the emerging SM 3.0. However, federated learning is susceptible to some forms of data leakage through model inversion attacks. Such attacks occur through the analysis of participants’ uploaded model updates. Model inversion attacks can reveal private data and potentially undermine some critical reasons for employing federated learning paradigms. This article proposes novel differential privacy (DP)-based deep federated learning framework. We theoretically prove that our framework can fulfill DP’s requirements under distinct privacy levels by appropriately adjusting scaled variances of Gaussian noise. We then develop a Differentially Private Data-Level Perturbation (DP-DLP) mechanism to conceal any single data point’s impact on the training phase. Experiments on real-world datasets, specifically the social media 3.0, Iris, and Human Activity Recognition (HAR) datasets, demonstrate that the proposed mechanism can offer high privacy, enhanced utility, and elevated efficiency. Consequently, it simplifies the development of various DP-based FL models with different tradeoff preferences on data utility and privacy levels.

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Cited By

Salim SMoustafa NAlmorjan A(2025)Responsible Deep-Federated-Learning-Based Threat Detection for Satellite CommunicationsIEEE Internet of Things Journal10.1109/JIOT.2025.353188412:5(4807-4819)Online publication date: 1-Mar-2025
https://doi.org/10.1109/JIOT.2025.3531884
Salim SMoustafa NTurnbull B(2025)BFL-SC: A blockchain-enabled federated learning framework, with smart contracts, for securing social media-integrated internet of things systemsAd Hoc Networks10.1016/j.adhoc.2025.103760169(103760)Online publication date: Mar-2025
https://doi.org/10.1016/j.adhoc.2025.103760
Gao JLi PLaghari ASrivastava GGadekallu TAbbas SZhang J(2024)Incomplete Multiview Clustering via Semidiscrete Optimal Transport for Multimedia Data Mining in IoTACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362554820:6(1-20)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3625548
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Index Terms

Perturbation-enabled Deep Federated Learning for Preserving Internet of Things-based Social Networks
1. Security and privacy
  1. Security services
    1. Privacy-preserving protocols

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Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18, Issue 2s

June 2022

383 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3561949

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 October 2022

Online AM: 23 May 2022

Revised: 17 April 2022

Accepted: 06 April 2022

Received: 28 November 2021

Published in TOMM Volume 18, Issue 2s

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Cited By

Salim SMoustafa NAlmorjan A(2025)Responsible Deep-Federated-Learning-Based Threat Detection for Satellite CommunicationsIEEE Internet of Things Journal10.1109/JIOT.2025.353188412:5(4807-4819)Online publication date: 1-Mar-2025
https://doi.org/10.1109/JIOT.2025.3531884
Salim SMoustafa NTurnbull B(2025)BFL-SC: A blockchain-enabled federated learning framework, with smart contracts, for securing social media-integrated internet of things systemsAd Hoc Networks10.1016/j.adhoc.2025.103760169(103760)Online publication date: Mar-2025
https://doi.org/10.1016/j.adhoc.2025.103760
Gao JLi PLaghari ASrivastava GGadekallu TAbbas SZhang J(2024)Incomplete Multiview Clustering via Semidiscrete Optimal Transport for Multimedia Data Mining in IoTACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362554820:6(1-20)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3625548
Gu SChen Z(2024)A Deep Federated Learning-Based User Credit Evaluation Model Under Financial Internet of Things ScenariosJournal of Circuits, Systems and Computers10.1142/S021812662450311033:17Online publication date: 21-Aug-2024
https://doi.org/10.1142/S0218126624503110
ElZemity AArief B(2024)Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics62450.2024.00072(331-338)Online publication date: 19-Aug-2024
https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics62450.2024.00072
Salim SMoustafa NHassanian MOrmod DSlay J(2024)Deep-Federated-Learning-Based Threat Detection Model for Extreme Satellite CommunicationsIEEE Internet of Things Journal10.1109/JIOT.2023.330162611:3(3853-3867)Online publication date: 1-Feb-2024
https://doi.org/10.1109/JIOT.2023.3301626
Abbas ZAhmad SSyed MAnjum ARehman S(2024)Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant ArchitectureIEEE Access10.1109/ACCESS.2023.334402912(10548-10574)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3344029
Ye SHuang QXia H(2024)Personalized movie recommendation in IoT-enhanced systems using graph convolutional network and multi-layer perceptronScientific Reports10.1038/s41598-024-76587-414:1Online publication date: 25-Oct-2024
https://doi.org/10.1038/s41598-024-76587-4
Jafarigol ETrafalis TRazzaghi TZamankhani M(2024)Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and LimitationsDynamics of Disasters10.1007/978-3-031-74006-0_4(87-121)Online publication date: 25-Sep-2024
https://doi.org/10.1007/978-3-031-74006-0_4
Shahid AImteaj ABadsha SHossain M(2023)Assessing Wearable Human Activity Recognition Systems Against Data Poisoning Attacks in Differentially-Private Federated Learning2023 IEEE International Conference on Smart Computing (SMARTCOMP)10.1109/SMARTCOMP58114.2023.00085(355-360)Online publication date: Jun-2023
https://doi.org/10.1109/SMARTCOMP58114.2023.00085
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