Privacy preserved data sharing using blockchain and support vector machine for industrial IOT applications
The Industrial Internet of Things (IIoT) paradigm's fast expansion in the amount of information created from linked devices creates new opportunities for improving the service quality and applications through sharing of data. Data security is a significant problem since training and Support Vec...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917423002271 |
| _version_ | 1827815762380718080 |
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| author | Arvind Kumar Pandey Rini Saxena Aishwary Awasthi M.P. Sunil |
| author_facet | Arvind Kumar Pandey Rini Saxena Aishwary Awasthi M.P. Sunil |
| author_sort | Arvind Kumar Pandey |
| collection | DOAJ |
| description | The Industrial Internet of Things (IIoT) paradigm's fast expansion in the amount of information created from linked devices creates new opportunities for improving the service quality and applications through sharing of data. Data security is a significant problem since training and Support Vector Machine (SVM) classifier often involves compiling tagged IoT data from several organizations. Beyond causing the suppliers to lose money, disclosing sensitive information might cause significant problems. To solve these issues, introduced a secure SVM, which is a Privacy-Preserving SVM (PP-SVM) training method using block chain-based encoded IoT data, to fill the void between ideal assumptions and practical restrictions. IoT messages are encrypted before being stored on a decentralized system because Block chain offers safe and dependable data exchange platforms across several data sources. Using a homomorphic cryptographic algorithm, Paillier, reliable modules have been developed, such as protected algebraic multiplying and protected comparison. Furthermore, a protected SVM learning algorithm that only needs two conversations during a single step has been created. According to stringent security assessment, proposed approach guarantees SVM model parameters for data professionals and secrecy of sensitive information for each data source. Extensive testing backs up effectiveness of the suggested plan. |
| first_indexed | 2024-03-12T00:05:15Z |
| format | Article |
| id | doaj.art-81c1354823cf4d85bf44dc729b833307 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-03-12T00:05:15Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-81c1354823cf4d85bf44dc729b8333072023-09-17T04:57:30ZengElsevierMeasurement: Sensors2665-91742023-10-0129100891Privacy preserved data sharing using blockchain and support vector machine for industrial IOT applicationsArvind Kumar Pandey0Rini Saxena1Aishwary Awasthi2M.P. Sunil3Department of Computer Science and Information Technology, ARKA JAIN University, Jamshedpur, Jharkahnd, India; Corresponding author.Department of Computer Science & Engineering, Chandigarh Engineering College Jhanjeri, Mohali, IndiaDepartment of Mechanical Engineering, Sanskriti University, Mathura, Uttar Pradesh, IndiaDepartment of Electronics and Communication Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), IndiaThe Industrial Internet of Things (IIoT) paradigm's fast expansion in the amount of information created from linked devices creates new opportunities for improving the service quality and applications through sharing of data. Data security is a significant problem since training and Support Vector Machine (SVM) classifier often involves compiling tagged IoT data from several organizations. Beyond causing the suppliers to lose money, disclosing sensitive information might cause significant problems. To solve these issues, introduced a secure SVM, which is a Privacy-Preserving SVM (PP-SVM) training method using block chain-based encoded IoT data, to fill the void between ideal assumptions and practical restrictions. IoT messages are encrypted before being stored on a decentralized system because Block chain offers safe and dependable data exchange platforms across several data sources. Using a homomorphic cryptographic algorithm, Paillier, reliable modules have been developed, such as protected algebraic multiplying and protected comparison. Furthermore, a protected SVM learning algorithm that only needs two conversations during a single step has been created. According to stringent security assessment, proposed approach guarantees SVM model parameters for data professionals and secrecy of sensitive information for each data source. Extensive testing backs up effectiveness of the suggested plan.http://www.sciencedirect.com/science/article/pii/S2665917423002271Support vector machineMachine learningBlock chainIndustrial IoTPrivacyData security |
| spellingShingle | Arvind Kumar Pandey Rini Saxena Aishwary Awasthi M.P. Sunil Privacy preserved data sharing using blockchain and support vector machine for industrial IOT applications Measurement: Sensors Support vector machine Machine learning Block chain Industrial IoT Privacy Data security |
| title | Privacy preserved data sharing using blockchain and support vector machine for industrial IOT applications |
| title_full | Privacy preserved data sharing using blockchain and support vector machine for industrial IOT applications |
| title_fullStr | Privacy preserved data sharing using blockchain and support vector machine for industrial IOT applications |
| title_full_unstemmed | Privacy preserved data sharing using blockchain and support vector machine for industrial IOT applications |
| title_short | Privacy preserved data sharing using blockchain and support vector machine for industrial IOT applications |
| title_sort | privacy preserved data sharing using blockchain and support vector machine for industrial iot applications |
| topic | Support vector machine Machine learning Block chain Industrial IoT Privacy Data security |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423002271 |
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