More Web Proxy on the site http://driver.im/

research-article

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.

References

[1]

Yunlong Lu, Xiaohong Huang, Yueyue Dai, Sabita Maharjan, and Yan Zhang. 2020. Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Transactions on Industrial Informatics 16, 6 (2020), 4177–4186.

[2]

Harsh Kasyap and Somanath Tripathy. 2021. Privacy-preserving decentralized learning framework for healthcare system. ACM Transactions on Multimedia Computing, Communications, and Applications 17, 2s (2021), 1–24.

Digital Library

[3]

Fatimah Alzamzami and Abdulmotaleb El Saddik. 2021. Monitoring cyber SentiHate social behavior during COVID-19 pandemic in North America. IEEE Access 9 (2021), 91184–91208.

[4]

Nguyen Truong, Kai Sun, Siyao Wang, Florian Guitton, and YiKe Guo. 2021. Privacy preservation in federated learning: An insightful survey from the GDPR perspective. Computers & Security 110 (2021), 102402.

Digital Library

[5]

Sanchari Das, Jayati Dev, and Kaushik Srinivasan. 2018. Modularity is the key a new approach to social media privacy policies. In Proceedings of the 7th Mexican Conference on Human-computer Interaction. 1–4.

Digital Library

[6]

2021. 13 Critical Data Breach Stats for Australian Businesses in 2021 | UpGuard. Retrieved September 14, 2021 from https://www.upguard.com/blog/australian-data-breach-stats.

[7]

Sara Salim, Nour Moustafa, and Benjamin Turnbull. 2020. Privacy-encoding models for preserving utility of machine learning algorithms in social media. In Proceedings of the 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 856–863.

[8]

Weiwei Sun, Jiantao Zhou, Shuyuan Zhu, and Yuan Yan Tang. 2018. Robust privacy-preserving image sharing over online social networks (OSNs). ACM Transactions on Multimedia Computing, Communications, and Applications 14, 1 (2018), 1–22.

Digital Library

[9]

Ming Cheung, Weiwei Sun, James She, and Jiantao Zhou. 2022. Social network analytic-based online counterfeit seller detection using user shared images. ACM Transactions on Multimedia Computing, Communications, and Applications (2022).

Digital Library

[10]

Kang Wei, Jun Li, Chuan Ma, Ming Ding, and H. Vincent Poor. 2021. Differentially private federated learning: Algorithm, analysis and optimization. In Proceedings of the Federated Learning Systems. Springer, 51–78.

[11]

Tom Titcombe, Adam J. Hall, Pavlos Papadopoulos, and Daniele Romanini. 2021. Practical defences against model inversion attacks for split neural networks. arXiv:2104.05743. Retrieved from https://arxiv.org/abs/2104.05743.

[12]

Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015. Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 1322–1333.

Digital Library

[13]

Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller. 2020. Inverting gradients–how easy is it to break privacy in federated learning? In Advances in Neural Information Processing Systems, Vol. 33. Curran Associates, Inc., 16937–16947. https://proceedings.neurips.cc/paper/2020/file/c4ede56bbd98819ae6112b20ac6bf145-Paper.pdf.

[14]

Parjanay Sharma, Siddhant Jain, Shashank Gupta, and Vinay Chamola. 2021. Role of machine learning and deep learning in securing 5G-driven industrial IoT applications. Ad Hoc Networks 123 (2021), 102685.

Digital Library

[15]

Wei Zhang, Ting Yao, Shiai Zhu, and Abdulmotaleb El Saddik. 2019. Deep learning–based multimedia analytics: A review. ACM Transactions on Multimedia Computing, Communications, and Applications 15, 1s (2019), 1–26.

Digital Library

[16]

Yilin Kang, Yong Liu, Ben Niu, Xinyi Tong, Likun Zhang, and Weiping Wang. 2020. Input perturbation: A new paradigm between central and local differential privacy. arXiv:2002.08570. Retrieved from https://arxiv.org/abs/2002.08570.

[17]

Yi Liu, J. Q. James, Jiawen Kang, Dusit Niyato, and Shuyu Zhang. 2020. Privacy-preserving traffic flow prediction: A federated learning approach. IEEE Internet of Things Journal 7, 8 (2020), 7751–7763.

[18]

Bin Jia, Xiaosong Zhang, Jiewen Liu, Yang Zhang, Ke Huang, and Yongquan Liang. 2021. Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in IIoT. IEEE Transactions on Industrial Informatics 18, 6 (2021), 1–1.

[19]

Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2016. Calibrating noise to sensitivity in private data analysis. Journal of Privacy and Confidentiality 7, 3 (2016), 17–51.

[20]

Sara Salim, Benjamin Turnbull, and Nour Moustafa. 2021. A blockchain-enabled explainable federated learning for securing Internet-of-Things-based social media 3.0 networks. IEEE Transactions on Computational Social Systems (2021), 1–17.

[21]

Jinxue Zhang, Jingchao Sun, Rui Zhang, Yanchao Zhang, and Xia Hu. 2018. Privacy-preserving social media data outsourcing. In Proceedings of the IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, 1106–1114.

Digital Library

[22]

Anabel Quan-Haase and Isioma Elueze. 2018. Revisiting the privacy paradox: Concerns and protection strategies in the social media experiences of older adults. In Proceedings of the 9th International Conference on Social Media and Society. 150–159.

Digital Library

[23]

Anitha Anandhan, Liyana Shuib, Maizatul Akmar Ismail, and Ghulam Mujtaba. 2018. Social media recommender systems: Review and open research issues. IEEE Access 6 (2018), 15608–15628.

[24]

Pathum Chamikara Mahawaga Arachchige, Peter Bertok, Ibrahim Khalil, Dongxi Liu, Seyit Camtepe, and Mohammed Atiquzzaman. 2020. A trustworthy privacy preserving framework for machine learning in industrial IoT systems. IEEE Transactions on Industrial Informatics 16, 9 (2020), 6092–6102.

[25]

Ming Cheung, James She, Alvin Junus, and Lei Cao. 2016. Prediction of virality timing using cascades in social media. ACM Transactions on Multimedia Computing, Communications, and Applications 13, 1 (2016), 1–23.

Digital Library

[26]

Ming Cheung and James She. 2016. Evaluating the privacy risk of user-shared images. ACM Transactions on Multimedia Computing, Communications, and Applications 12, 4s (2016), 1–21.

Digital Library

[27]

Chunhua Ju, Jie Wang, and Chonghuan Xu. 2019. A novel application recommendation method combining social relationship and trust relationship for future Internet of Things. Multimedia Tools and Applications 78, 21 (2019), 29867–29880.

[28]

Vikas Hassija, Vinay Chamola, Vikas Saxena, Divyansh Jain, Pranav Goyal, and Biplab Sikdar. 2019. A survey on IoT security: Application areas, security threats, and solution architectures. IEEE Access 7 (2019), 82721–82743.

[29]

Aman Tahiliani, Vikas Hassija, Vinay Chamola, Salil S. Kanhere, and Mohsen Guizani. 2021. Privacy-preserving and incentivized contact tracing for covid-19 using blockchain. IEEE Internet of Things Magazine 4, 3 (2021), 72–79.

[30]

Simson L. Garfinkel. 2015. De-identification of personal information. National Institute of Standards and Technology (2015), 1–46.

[31]

Dingqi Yang, Bingqing Qu, and Philippe Cudré-Mauroux. 2018. Privacy-preserving social media data publishing for personalized ranking-based recommendation. IEEE Transactions on Knowledge and Data Engineering 31, 3 (2018), 507–520.

Digital Library

[32]

Navid Yazdanjue, Mohammad Fathian, and Babak Amiri. 2020. Evolutionary algorithms for k-anonymity in social networks based on clustering approach. The Computer Journal 63, 7 (2020), 1039–1062.

[33]

Ricardo Mendes and João P. Vilela. 2017. Privacy-preserving data mining: Methods, metrics, and applications. IEEE Access 5 (2017), 10562–10582.

[34]

Abdul Majeed and Sungchang Lee. 2021. Anonymization techniques for privacy preserving data publishing: A comprehensive survey. IEEE Access 9 (2021), 8512–8545.

[35]

Robin C. Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv:1712.07557. Retrieved from https://arxiv.org/abs/1712.07557.

[36]

Nuria Rodríguez-Barroso, Goran Stipcich, Daniel Jiménez-López, José Antonio Ruiz-Millán, Eugenio Martínez-Cámara, Gerardo González-Seco, M. Victoria Luzón, Miguel Angel Veganzones, and Francisco Herrera. 2020. Federated learning and differential privacy: Software tools analysis, the Sherpa. ai FL framework and methodological guidelines for preserving data privacy. Information Fusion 64 (2020), 270–292.

[37]

Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou. 2019. A hybrid approach to privacy-preserving federated learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security. 1–11.

Digital Library

[38]

Sara Salim, Benjamin Turnbull, and Nour Moustafa. 2022. Data analytics of social media 3.0: Privacy protection perspectives for integrating social media and Internet of Things (SM-IoT) systems. Ad Hoc Networks 128 (2022), 102786. DOI:

Digital Library

[39]

Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. Retrieved Accessed 27 January, 2022 from http://archive.ics.uci.edu/ml.

[40]

Luca Oneto Xavier Parra Davide Anguita, Alessandro Ghio and Jorge L. 2013. A Public Domain Dataset for Human Activity Recognition Using Smartphones. Retrieved January 27, 2022 from https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones.

[41]

Xudong Zhu, Hui Li, and Yang Yu. 2018. Blockchain-based privacy preserving deep learning. In Proceedings of the International Conference on Information Security and Cryptology. Springer, 370–383.

[42]

Shaohua Wan, Lianyong Qi, Xiaolong Xu, Chao Tong, and Zonghua Gu. 2020. Deep learning models for real-time human activity recognition with smartphones. Mobile Networks and Applications 25, 2 (2020), 743–755.

Digital Library

Cited By

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
Show More Cited By

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

Recommendations

Differential privacy in deep learning: A literature survey
Abstract
The widespread adoption of deep learning is facilitated in part by the availability of large-scale data for training desirable models. However, these data may involve sensitive personal information, which raises privacy concerns for data ...
Privacy preserving data obfuscation for inherently clustered data

Privacy is defined as the freedom from unauthorised intrusion. The availability of public records along with intelligent search engines and data mining tools allow easy access to useful information. They also serve as a haven for individuals with ...
A review of privacy preserving models for multi-party data release framework
WIR '16: Proceedings of the ACM Symposium on Women in Research 2016

Nowadays, with the improvement of internet technology and advancement in distributed computing data is increasing rapidly. There is a need of information sharing between organizations. Ideally, we wish to share data from multiple private databases and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

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

Issue’s Table of Contents

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].

Publisher

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

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
558
Total Downloads

Downloads (Last 12 months)155
Downloads (Last 6 weeks)21

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

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
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
Al-Wesabi FMengash HMarzouk RAlruwais NAllafi RAlabdan RAlharbi MGupta D(2023)Pelican Optimization Algorithm with Federated Learning Driven Attack Detection model in Internet of Things environmentFuture Generation Computer Systems10.1016/j.future.2023.05.029148(118-127)Online publication date: Nov-2023
https://doi.org/10.1016/j.future.2023.05.029
Kapila PSingh B(2022)Federated Learning Method for Local Differential Privacy in IoT Networks2022 International Conference on Futuristic Technologies (INCOFT)10.1109/INCOFT55651.2022.10094522(1-6)Online publication date: 25-Nov-2022
https://doi.org/10.1109/INCOFT55651.2022.10094522

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents