A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applications
Abstract The rapid advancement of key technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and edge-cloud computing has significantly accelerated the transformation toward smart industries across various domains, including finance, manufacturing, and healthcare. Edge and...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-15077-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849766940846850048 |
|---|---|
| author | Dileep Kumar Murala K. Vara Prasada Rao Veera Ankalu Vuyyuru Beakal Gizachew Assefa |
| author_facet | Dileep Kumar Murala K. Vara Prasada Rao Veera Ankalu Vuyyuru Beakal Gizachew Assefa |
| author_sort | Dileep Kumar Murala |
| collection | DOAJ |
| description | Abstract The rapid advancement of key technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and edge-cloud computing has significantly accelerated the transformation toward smart industries across various domains, including finance, manufacturing, and healthcare. Edge and cloud computing offer low-cost, scalable, and on-demand computational resources, enabling service providers to deliver intelligent data analytics and real-time insights to end-users. However, despite their potential, the practical adoption of these technologies faces critical challenges, particularly concerning data privacy and security. AI models, especially in distributed environments, may inadvertently retain and leak sensitive training data, exposing users to privacy risks in the event of malicious attacks. To address these challenges, this study proposes a privacy-preserving, service-oriented microservice architecture tailored for intelligent Industrial IoT (IIoT) applications. The architecture integrates Differential Privacy (DP) mechanisms into the machine learning pipeline to safeguard sensitive information. It supports both centralised and distributed deployments, promoting flexible, scalable, and secure analytics. We developed and evaluated differentially private models, including Radial Basis Function Networks (RBFNs), across a range of privacy budgets ( $$\varepsilon$$ ), using both real-world and synthetic IoT datasets. Experimental evaluations using RBFNs demonstrate that the framework maintains high predictive accuracy (up to 96.72%) with acceptable privacy guarantees for budgets $$\varepsilon \ge 0.5$$ . Furthermore, the microservice-based deployment achieves an average latency reduction of 28.4% compared to monolithic baselines. These results confirm the effectiveness and practicality of the proposed architecture in delivering privacy-preserving, efficient, and scalable intelligence for IIoT environments. Additionally, the microservice-based design enhanced computational efficiency and reduced latency through dynamic service orchestration. This research demonstrates the feasibility of deploying robust, privacy-conscious AI services in IIoT environments, paving the way for secure, intelligent, and scalable industrial systems. |
| format | Article |
| id | doaj-art-36c16b6a1b6a4e9eae9cfa21587d25f2 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-36c16b6a1b6a4e9eae9cfa21587d25f22025-08-20T03:04:25ZengNature PortfolioScientific Reports2045-23222025-08-0115112010.1038/s41598-025-15077-7A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applicationsDileep Kumar Murala0K. Vara Prasada Rao1Veera Ankalu Vuyyuru2Beakal Gizachew Assefa3Department of Computer Science and Engineering, Faculty of Science and Technology, ICFAI Foundation for Higher EducationDepartment of Computer Science and Engineering, Faculty of Science and Technology, ICFAI Foundation for Higher EducationDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education FoundationSchool of Information Technology and Engineering, Addis Ababa UniversityAbstract The rapid advancement of key technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and edge-cloud computing has significantly accelerated the transformation toward smart industries across various domains, including finance, manufacturing, and healthcare. Edge and cloud computing offer low-cost, scalable, and on-demand computational resources, enabling service providers to deliver intelligent data analytics and real-time insights to end-users. However, despite their potential, the practical adoption of these technologies faces critical challenges, particularly concerning data privacy and security. AI models, especially in distributed environments, may inadvertently retain and leak sensitive training data, exposing users to privacy risks in the event of malicious attacks. To address these challenges, this study proposes a privacy-preserving, service-oriented microservice architecture tailored for intelligent Industrial IoT (IIoT) applications. The architecture integrates Differential Privacy (DP) mechanisms into the machine learning pipeline to safeguard sensitive information. It supports both centralised and distributed deployments, promoting flexible, scalable, and secure analytics. We developed and evaluated differentially private models, including Radial Basis Function Networks (RBFNs), across a range of privacy budgets ( $$\varepsilon$$ ), using both real-world and synthetic IoT datasets. Experimental evaluations using RBFNs demonstrate that the framework maintains high predictive accuracy (up to 96.72%) with acceptable privacy guarantees for budgets $$\varepsilon \ge 0.5$$ . Furthermore, the microservice-based deployment achieves an average latency reduction of 28.4% compared to monolithic baselines. These results confirm the effectiveness and practicality of the proposed architecture in delivering privacy-preserving, efficient, and scalable intelligence for IIoT environments. Additionally, the microservice-based design enhanced computational efficiency and reduced latency through dynamic service orchestration. This research demonstrates the feasibility of deploying robust, privacy-conscious AI services in IIoT environments, paving the way for secure, intelligent, and scalable industrial systems.https://doi.org/10.1038/s41598-025-15077-7Industrial Internet of Things (IIoT)Health care sectorDifferential privacyMicroservices architectureEdge-cloud computingPrivacy |
| spellingShingle | Dileep Kumar Murala K. Vara Prasada Rao Veera Ankalu Vuyyuru Beakal Gizachew Assefa A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applications Scientific Reports Industrial Internet of Things (IIoT) Health care sector Differential privacy Microservices architecture Edge-cloud computing Privacy |
| title | A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applications |
| title_full | A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applications |
| title_fullStr | A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applications |
| title_full_unstemmed | A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applications |
| title_short | A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applications |
| title_sort | service oriented microservice framework for differential privacy based protection in industrial iot smart applications |
| topic | Industrial Internet of Things (IIoT) Health care sector Differential privacy Microservices architecture Edge-cloud computing Privacy |
| url | https://doi.org/10.1038/s41598-025-15077-7 |
| work_keys_str_mv | AT dileepkumarmurala aserviceorientedmicroserviceframeworkfordifferentialprivacybasedprotectioninindustrialiotsmartapplications AT kvaraprasadarao aserviceorientedmicroserviceframeworkfordifferentialprivacybasedprotectioninindustrialiotsmartapplications AT veeraankaluvuyyuru aserviceorientedmicroserviceframeworkfordifferentialprivacybasedprotectioninindustrialiotsmartapplications AT beakalgizachewassefa aserviceorientedmicroserviceframeworkfordifferentialprivacybasedprotectioninindustrialiotsmartapplications AT dileepkumarmurala serviceorientedmicroserviceframeworkfordifferentialprivacybasedprotectioninindustrialiotsmartapplications AT kvaraprasadarao serviceorientedmicroserviceframeworkfordifferentialprivacybasedprotectioninindustrialiotsmartapplications AT veeraankaluvuyyuru serviceorientedmicroserviceframeworkfordifferentialprivacybasedprotectioninindustrialiotsmartapplications AT beakalgizachewassefa serviceorientedmicroserviceframeworkfordifferentialprivacybasedprotectioninindustrialiotsmartapplications |