Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image Approach
Deeper integration of cross-organizational business process sharing and process mining has advanced the Industrial Internet. Privacy breaches and data security risks limit its use. Scrambling or anonymizing event data frequently preserves privacy in established studies. The scrambling mechanism or r...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2321556 |
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| _version_ | 1850064206180646912 |
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| author | Xianwen Fang Mengyao Li |
| author_facet | Xianwen Fang Mengyao Li |
| author_sort | Xianwen Fang |
| collection | DOAJ |
| description | Deeper integration of cross-organizational business process sharing and process mining has advanced the Industrial Internet. Privacy breaches and data security risks limit its use. Scrambling or anonymizing event data frequently preserves privacy in established studies. The scrambling mechanism or random noise injection corrupts event log process information and lowers process mining outcomes. This research presents a blockchain-based privacy-aware reversible shared image approach using chaotic image and privacy-aware theory for privacy-preserving process mining. Avoiding data loss, disclosure concerns, correlation attacks, and encrypted sharing is possible with the method. First, process data is turned into color images with chaotic image encryption to safeguard privacy and allow reversible reproduction. Second, the on-chain-off-chain paradigm helps handle information lightly; finally, attribute encryption of multi-view event data for correlation resistance and on-demand data encryption sharing. Simulations on common datasets reveal that: 1. The system performance of the proposed method outperforms the baseline method by 57%. 2. The strategy greatly enhances categorical and numerical data privacy. 3. It performs better in event data privacy protection and process mining fitness and precision. The proposed method ensures the secure flow of cross-organizational information in the Industrial Internet and provides a novel privacy-secure computational approach for the growing Artificial Intelligence. |
| format | Article |
| id | doaj-art-c1a7c53efdb34e32a97c1839b6f6213f |
| institution | DOAJ |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-c1a7c53efdb34e32a97c1839b6f6213f2025-08-20T02:49:22ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2321556Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image ApproachXianwen Fang0Mengyao Li1School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, ChinaSchool of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, ChinaDeeper integration of cross-organizational business process sharing and process mining has advanced the Industrial Internet. Privacy breaches and data security risks limit its use. Scrambling or anonymizing event data frequently preserves privacy in established studies. The scrambling mechanism or random noise injection corrupts event log process information and lowers process mining outcomes. This research presents a blockchain-based privacy-aware reversible shared image approach using chaotic image and privacy-aware theory for privacy-preserving process mining. Avoiding data loss, disclosure concerns, correlation attacks, and encrypted sharing is possible with the method. First, process data is turned into color images with chaotic image encryption to safeguard privacy and allow reversible reproduction. Second, the on-chain-off-chain paradigm helps handle information lightly; finally, attribute encryption of multi-view event data for correlation resistance and on-demand data encryption sharing. Simulations on common datasets reveal that: 1. The system performance of the proposed method outperforms the baseline method by 57%. 2. The strategy greatly enhances categorical and numerical data privacy. 3. It performs better in event data privacy protection and process mining fitness and precision. The proposed method ensures the secure flow of cross-organizational information in the Industrial Internet and provides a novel privacy-secure computational approach for the growing Artificial Intelligence.https://www.tandfonline.com/doi/10.1080/08839514.2024.2321556 |
| spellingShingle | Xianwen Fang Mengyao Li Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image Approach Applied Artificial Intelligence |
| title | Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image Approach |
| title_full | Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image Approach |
| title_fullStr | Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image Approach |
| title_full_unstemmed | Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image Approach |
| title_short | Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image Approach |
| title_sort | privacy preserving process mining a blockchain based privacy aware reversible shared image approach |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2321556 |
| work_keys_str_mv | AT xianwenfang privacypreservingprocessminingablockchainbasedprivacyawarereversiblesharedimageapproach AT mengyaoli privacypreservingprocessminingablockchainbasedprivacyawarereversiblesharedimageapproach |