EdgeSecureDP: Strengthening IoHTs Differential Privacy Through Graphvariate Skellam
The Internet of Health Things (IoHTs) has transformed healthcare systems, facilitating remote patient monitoring and personalized treatment. Federated Learning (FL) has emerged as a promising solution, enabling decentralized devices to collaboratively train machine learning models while ensuring pri...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10817548/ |
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| author | Mohamed Amjath Shagufta Henna |
| author_facet | Mohamed Amjath Shagufta Henna |
| author_sort | Mohamed Amjath |
| collection | DOAJ |
| description | The Internet of Health Things (IoHTs) has transformed healthcare systems, facilitating remote patient monitoring and personalized treatment. Federated Learning (FL) has emerged as a promising solution, enabling decentralized devices to collaboratively train machine learning models while ensuring privacy and security in healthcare applications. Differential Privacy (DP) is used to enhance privacy in FL frameworks by injecting controlled noise into data or model updates, preventing attackers from extracting specific information. However, existing DP mechanisms, such as the Gaussian mechanism and Univariate Skellam struggle to balance privacy-utility trade-off for graph-based data like drug-drug interactions. These mechanisms treat data points independently, failing to account for the complex interconnections between nodes (drugs) and edges (interactions), leaves the network vulnerable to structural attacks that can reverse-engineer relationships, thus limiting the security of collaborative drug discovery. To address these limitations, this work proposes Graphvariate Skellam, a novel DP approach that leverages graph structure information in FL settings, referred to as EdgeSecureDP. By exploiting the structural information encoded in graph edges, this method offers enhanced privacy protection. Experimental results and theoretical analysis demonstrate that Graphvariate Skellam effectively preserves privacy (<inline-formula> <tex-math notation="LaTeX">$15 \lt \epsilon \leq 20$ </tex-math></inline-formula>) while achieving 78% accuracy in IoHT environments, making it a robust solution for privacy-preserving healthcare applications. |
| format | Article |
| id | doaj-art-82957b2111204806b5294177e9ad34aa |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-82957b2111204806b5294177e9ad34aa2025-01-03T00:01:53ZengIEEEIEEE Access2169-35362025-01-01131179119210.1109/ACCESS.2024.352374910817548EdgeSecureDP: Strengthening IoHTs Differential Privacy Through Graphvariate SkellamMohamed Amjath0https://orcid.org/0009-0006-2596-8642Shagufta Henna1https://orcid.org/0000-0002-8753-5467Department of Computing, Atlantic Technological University, Donegal, IrelandDepartment of Computing, Atlantic Technological University, Donegal, IrelandThe Internet of Health Things (IoHTs) has transformed healthcare systems, facilitating remote patient monitoring and personalized treatment. Federated Learning (FL) has emerged as a promising solution, enabling decentralized devices to collaboratively train machine learning models while ensuring privacy and security in healthcare applications. Differential Privacy (DP) is used to enhance privacy in FL frameworks by injecting controlled noise into data or model updates, preventing attackers from extracting specific information. However, existing DP mechanisms, such as the Gaussian mechanism and Univariate Skellam struggle to balance privacy-utility trade-off for graph-based data like drug-drug interactions. These mechanisms treat data points independently, failing to account for the complex interconnections between nodes (drugs) and edges (interactions), leaves the network vulnerable to structural attacks that can reverse-engineer relationships, thus limiting the security of collaborative drug discovery. To address these limitations, this work proposes Graphvariate Skellam, a novel DP approach that leverages graph structure information in FL settings, referred to as EdgeSecureDP. By exploiting the structural information encoded in graph edges, this method offers enhanced privacy protection. Experimental results and theoretical analysis demonstrate that Graphvariate Skellam effectively preserves privacy (<inline-formula> <tex-math notation="LaTeX">$15 \lt \epsilon \leq 20$ </tex-math></inline-formula>) while achieving 78% accuracy in IoHT environments, making it a robust solution for privacy-preserving healthcare applications.https://ieeexplore.ieee.org/document/10817548/Differential privacyfederated learningunivariate Skellamgraph-based differential privacy |
| spellingShingle | Mohamed Amjath Shagufta Henna EdgeSecureDP: Strengthening IoHTs Differential Privacy Through Graphvariate Skellam IEEE Access Differential privacy federated learning univariate Skellam graph-based differential privacy |
| title | EdgeSecureDP: Strengthening IoHTs Differential Privacy Through Graphvariate Skellam |
| title_full | EdgeSecureDP: Strengthening IoHTs Differential Privacy Through Graphvariate Skellam |
| title_fullStr | EdgeSecureDP: Strengthening IoHTs Differential Privacy Through Graphvariate Skellam |
| title_full_unstemmed | EdgeSecureDP: Strengthening IoHTs Differential Privacy Through Graphvariate Skellam |
| title_short | EdgeSecureDP: Strengthening IoHTs Differential Privacy Through Graphvariate Skellam |
| title_sort | edgesecuredp strengthening iohts differential privacy through graphvariate skellam |
| topic | Differential privacy federated learning univariate Skellam graph-based differential privacy |
| url | https://ieeexplore.ieee.org/document/10817548/ |
| work_keys_str_mv | AT mohamedamjath edgesecuredpstrengtheningiohtsdifferentialprivacythroughgraphvariateskellam AT shaguftahenna edgesecuredpstrengtheningiohtsdifferentialprivacythroughgraphvariateskellam |