A Reliable Application of MPC for Securing the Tri-Training Algorithm
Due to the widespread use of distributed data mining techniques in a variety of areas, the issue of protecting the privacy of sensitive data has received increasing attention in recent years. Privacy-preserving distributed data mining (PPDDM) focuses on decentralized data analysis without the disclo...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10092759/ |
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| author | Hendra Kurniawan Masahiro Mambo |
| author_facet | Hendra Kurniawan Masahiro Mambo |
| author_sort | Hendra Kurniawan |
| collection | DOAJ |
| description | Due to the widespread use of distributed data mining techniques in a variety of areas, the issue of protecting the privacy of sensitive data has received increasing attention in recent years. Privacy-preserving distributed data mining (PPDDM) focuses on decentralized data analysis without the disclosure of sensitive information from data owner. However, the previous PPDDM mostly works on a limited amount of labeled data. In contrast to the real world, unlabeled data is abundance and labeled data is scarce. The objectives of this paper are to study and to analyze privacy-preserving properties of semi-supervised learning (SSL) algorithm with the combination of labeled and unlabeled data, where data is distributed among multiple data owners. In this paper we propose a Privacy-preserving Distributed Data Mining (PPDDM) method by designing a reliable application of secure MPC to semi-supervised tri-training algorithms. We simulate the original tri-training algorithm and tri-training algorithm with secure MPC using a different types of classifiers and datasets. The simulation results show that tri-training in secure MPC has almost same accuracy compared to original tri-training algorithm. We also compare execution time in addition to performance evaluation of tri-training in secure and the original tri-training algorithms. |
| format | Article |
| id | doaj-art-12464a3670ba420fbd3fdbca3655c8a1 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-12464a3670ba420fbd3fdbca3655c8a12025-08-20T03:31:21ZengIEEEIEEE Access2169-35362023-01-0111347183473510.1109/ACCESS.2023.326490310092759A Reliable Application of MPC for Securing the Tri-Training AlgorithmHendra Kurniawan0https://orcid.org/0000-0003-4724-2093Masahiro Mambo1Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, JapanInstitute of Science and Engineering, Kanazawa University, Kanazawa, JapanDue to the widespread use of distributed data mining techniques in a variety of areas, the issue of protecting the privacy of sensitive data has received increasing attention in recent years. Privacy-preserving distributed data mining (PPDDM) focuses on decentralized data analysis without the disclosure of sensitive information from data owner. However, the previous PPDDM mostly works on a limited amount of labeled data. In contrast to the real world, unlabeled data is abundance and labeled data is scarce. The objectives of this paper are to study and to analyze privacy-preserving properties of semi-supervised learning (SSL) algorithm with the combination of labeled and unlabeled data, where data is distributed among multiple data owners. In this paper we propose a Privacy-preserving Distributed Data Mining (PPDDM) method by designing a reliable application of secure MPC to semi-supervised tri-training algorithms. We simulate the original tri-training algorithm and tri-training algorithm with secure MPC using a different types of classifiers and datasets. The simulation results show that tri-training in secure MPC has almost same accuracy compared to original tri-training algorithm. We also compare execution time in addition to performance evaluation of tri-training in secure and the original tri-training algorithms.https://ieeexplore.ieee.org/document/10092759/Distributed data miningmulti-party computationprivacy-preservingsemi-supervised learningtri-training |
| spellingShingle | Hendra Kurniawan Masahiro Mambo A Reliable Application of MPC for Securing the Tri-Training Algorithm IEEE Access Distributed data mining multi-party computation privacy-preserving semi-supervised learning tri-training |
| title | A Reliable Application of MPC for Securing the Tri-Training Algorithm |
| title_full | A Reliable Application of MPC for Securing the Tri-Training Algorithm |
| title_fullStr | A Reliable Application of MPC for Securing the Tri-Training Algorithm |
| title_full_unstemmed | A Reliable Application of MPC for Securing the Tri-Training Algorithm |
| title_short | A Reliable Application of MPC for Securing the Tri-Training Algorithm |
| title_sort | reliable application of mpc for securing the tri training algorithm |
| topic | Distributed data mining multi-party computation privacy-preserving semi-supervised learning tri-training |
| url | https://ieeexplore.ieee.org/document/10092759/ |
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