Secure Multi-Party Computation for Collaborative Data Analysis
A potent cryptographic mechanism called Secure Multi-Party Computation (SMPC) has evolved that allows numerous participants to work together and execute data analytic tasks while maintaining the privacy and secrecy of their individual data. In several fields, like healthcare, finance, and the social...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04034.pdf |
| _version_ | 1797775982455685120 |
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| author | Alghamdi Wajdi Salama Reda Sirija M. Abbas Ahmed Radie Dilnoza Kholmurodova |
| author_facet | Alghamdi Wajdi Salama Reda Sirija M. Abbas Ahmed Radie Dilnoza Kholmurodova |
| author_sort | Alghamdi Wajdi |
| collection | DOAJ |
| description | A potent cryptographic mechanism called Secure Multi-Party Computation (SMPC) has evolved that allows numerous participants to work together and execute data analytic tasks while maintaining the privacy and secrecy of their individual data. In several fields, like healthcare, finance, and the social sciences, where numerous stakeholders must exchange and evaluate sensitive information without disclosing it to others, collaborative data analysis is becoming more and more common. This study gives a thorough investigation of SMPC for group data analysis. The main goal is to give a thorough understanding of the SMPC’s guiding ideas, protocols, and applications while stressing the advantages and difficulties it presents for fostering safe cooperation among various data owners. In summary, this study offers a thorough and current examination of Secure Multi-Party Computation for Collaborative Data examination. It provides a thorough grasp of the SMPC deployment issues as well as the underlying ideas, protocols, and applications. The goal of the article is to function as a useful resource for researchers, professionals, and decision-makers interested in using SMPC to facilitate group data analysis while protecting confidentiality and privacy. |
| first_indexed | 2024-03-12T22:43:31Z |
| format | Article |
| id | doaj.art-af2576395d994d0688e346f71151feed |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-12T22:43:31Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-af2576395d994d0688e346f71151feed2023-07-21T09:28:35ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013990403410.1051/e3sconf/202339904034e3sconf_iconnect2023_04034Secure Multi-Party Computation for Collaborative Data AnalysisAlghamdi Wajdi0Salama Reda1Sirija M.2Abbas Ahmed Radie3Dilnoza Kholmurodova4Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz UniversityDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz UniversityAssistant Professor, Prince Shri Venkateshwara Padmavathy Engineering CollegeCollege of pharmacy, The Islamic universityTashkent State Pedagogical UniversityA potent cryptographic mechanism called Secure Multi-Party Computation (SMPC) has evolved that allows numerous participants to work together and execute data analytic tasks while maintaining the privacy and secrecy of their individual data. In several fields, like healthcare, finance, and the social sciences, where numerous stakeholders must exchange and evaluate sensitive information without disclosing it to others, collaborative data analysis is becoming more and more common. This study gives a thorough investigation of SMPC for group data analysis. The main goal is to give a thorough understanding of the SMPC’s guiding ideas, protocols, and applications while stressing the advantages and difficulties it presents for fostering safe cooperation among various data owners. In summary, this study offers a thorough and current examination of Secure Multi-Party Computation for Collaborative Data examination. It provides a thorough grasp of the SMPC deployment issues as well as the underlying ideas, protocols, and applications. The goal of the article is to function as a useful resource for researchers, professionals, and decision-makers interested in using SMPC to facilitate group data analysis while protecting confidentiality and privacy.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04034.pdf |
| spellingShingle | Alghamdi Wajdi Salama Reda Sirija M. Abbas Ahmed Radie Dilnoza Kholmurodova Secure Multi-Party Computation for Collaborative Data Analysis E3S Web of Conferences |
| title | Secure Multi-Party Computation for Collaborative Data Analysis |
| title_full | Secure Multi-Party Computation for Collaborative Data Analysis |
| title_fullStr | Secure Multi-Party Computation for Collaborative Data Analysis |
| title_full_unstemmed | Secure Multi-Party Computation for Collaborative Data Analysis |
| title_short | Secure Multi-Party Computation for Collaborative Data Analysis |
| title_sort | secure multi party computation for collaborative data analysis |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04034.pdf |
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