A Blockchain-Enabled Explainable Federated Learning for Securing Internet-of-Things-Based Social Media 3.0 Networks
Bibliographic Data
| ID | 22108404 |
|---|---|
| Authors | Sara Salim (0000-0002-0577-4625, University of Canberra), Benjamin Turnbull (0009-0006-1158-0428, University of Canberra), Nour Moustafa (0000-0001-6127-9349, University of Canberra) |
| Year | 2024 |
| Volume | 11 |
| Issue | 4 |
| Pages | 4681-4697 |
| Publication date | 2024-08-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2021.3134463 |
| OpenAlex | W4205622321 |
| Language | EN |
| Citations received | 3 |
| References cited | 36 |
Social media (SM) 3.0 integrates SM platforms, such as Facebook and Twitter, with the Internet of Things (IoT), and has a great potential to change how we interact with mobile devices, online platforms, and the world around us. This integration with end users produces large-scale and heterogeneous data sources that demand machine learning (ML)-based data analytics for decision-making and to provide security against ML and data privacy attacks. The development of privacy-aware ML models within a federated learning (FL) ecosystem can empower an entire network to learn from data in a decentralized manner. In this article, we propose a differentially privacy blockchain-based explainable FL (DP-BFL) framework by harnessing the ever-evolving power of SM 3.0 networks. This framework permits any Internet empowered device to partake and contribute data to a global privacy preserved model. In this framework, participants will upload the differentially private local updates to the miners of blockchain, where the local updates will be evaluated and rewarded. The experimental results obtained from real-world datasets, namely, SM 3.0 and MNIST, demonstrated that the proposed framework could achieve high utility, enhanced privacy, and elevated efficiency. More Specifically, the experimental analysis of our proposed framework reveals the following two key properties. First, our proposed DP-BFL yields noticeable performance improvements in the applied learning models with high privacy and comparable utility levels, in terms of accuracy and f-measure metrics, to a standard FL and centralized learning approaches under the restriction of privacy preservation. Second, given a certain number of the malicious entities, DP-BFL allowed an enhanced recognition of users' preferences in the SM 3.0 dataset and precise prediction of images' class in the MNIST dataset while mitigating the impact of the malicious entities' poisoned updates. Moreover, as the proposed DP-BFL attains DP on the local model's update, it is considered the same as the standard FL-based setting, along with some kinds of privacy preservation on the uploaded model's updates
Analytics · Big data · Blockchain · Computer security · Data mining · Data science · Differential privacy · Information privacy · Internet privacy · Social media · The Internet · Upload · World Wide Web · Blockchain Technology Applications and Security · Computer Science · Privacy-Preserving Technologies in Data · Privacy, Security, and Data Protection
| Citations per year | 1 |
|---|---|
| Citation span | 2023 - 2024 (2) |
| Highly cited | No |