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Research Article

Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image Approach

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Article: 2321556 | Received 31 Mar 2023, Accepted 15 Feb 2024, Published online: 05 Mar 2024

References

  • Ahuja, B., R. Doriya, S. Salunke, M. F. Hashmi, A. Gupta, and N. D. Bokde. 2023. HDIEA: High dimensional color image encryption architecture using five-dimensional Gauss-logistic and Lorenz system. Connection Science 35 (1):2175792. doi:10.1080/09540091.2023.2175792.
  • Al-Farsi, S., M. M. Rathore, and S. Bakiras. 2021. Security of blockchain-based supply chain management systems: Challenges and opportunities. Applied Sciences 11 (12):5585. doi:10.3390/app11125585.
  • Batista, E., A. Martínez-Ballesté, and A. Solanas. 2022. Privacy-preserving process mining: A microaggregation-Based approach. Journal of Information Security and Applications 68:103235. doi:10.1016/j.jisa.2022.103235.
  • Bhattacharya, P., S. Tanwar, U. Bodkhe, S. Tyagi, and N. Kumar. 2021. BinDaaS: Blockchain-Based deep-learning as-a-service in healthcare 4.0 applications. IEEE Transactions on Net work Science and Engineering 8 (2):1242–32. doi:10.1109/TNSE.2019.2961932.
  • Bu, F., C. Hu, Q. Zhang, C. Bai, L. T. Yang, and T. Baker. 2021. A cloud-edge-aided In-cremental high-order possibilistic c-means algorithm for medical data clustering. IEEE Tra-Nsactions on Fuzzy Systems 29 (1):148–55. doi:10.1109/TFUZZ.2020.3022080.
  • Cai, H., J. Sun, Z. Gao, and H. Zhang. 2022. A novel multi-wing chaotic system with FPGA implementation and application in image encryption. Journal of Real-Time Image Processing 19 (4):775–90. doi:10.1007/s11554-022-01220-4.
  • Elkoumy, G., A. Pankova, and M. Dumas. 2022. Differentially private release of event logs for process mining. arXiv:2201.03010. http://arxiv.org/abs/2201.03010.
  • Essa, Y. M., A. El-Mahalawy, G. Attiya, and A. El-Sayed. 2019. Parallel and distributed powerset generation using big data processing. Applied Artificial Intelligence 33 (13):1133–56. doi:10.1080/08839514.2019.1665262.
  • Fang, P., H. Liu, C. Wu, and M. Liu. 2022. A survey of image encryption algorithms based on chaotic system. The Visual Computer 39 (5):1975–2003. doi:10.1007/s00371-022-02459-5.
  • Fu, J., B. Cao, X. Wang, P. Zeng, W. Liang, and Y. Liu. 2022. BFS: A blockchain-based financing scheme for logistics company in supply chain finance. Connection Science 34 (1):1929–55. doi:10.1080/09540091.2022.2088698.
  • Hussain, M., W. Javed, O. Hakeem, A. Yousafzai, A. Younas, M. J. Awan, H. Nobanee, and A. M. Zain. 2021. Blockchain-based IoT devices in supply chain management: A systematic literature review. Sustainability 13 (24):13646. doi:10.3390/su132413646.
  • Kan, D., X. Fang, and Z. Gong. 2023. Event log privacy based on differential petri nets. Applied Artificial Intelligence 37 (1):2175109. doi:10.1080/08839514.2023.2175109.
  • Kerzel, U. 2021. Enterprise AI canvas integrating artificial intelligence into business. Applied Artificial Intelligence 35 (1):1–12. doi:10.1080/08839514.2020.1826146.
  • Kshetri, N. 2018. 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management 39:80–89. doi:10.1016/j.ijinfomgt.2017.12.005.
  • Lin, H., J. Hu, X. Wang, M. F. Alhamid, and M. J. Piran. 2021. Toward secure data fusion in industrial IoT using transfer learning. IEEE Transactions on Industrial Informatics 17 (10):7114–22. doi:10.1109/TII.2020.3038780.
  • Liu, C., H. Duan, Q. Zeng, M. Zhou, F. Lu, and J. Cheng. 2019. Towards comprehensive support for privacy preservation cross-organization business process mining. IEEE Transactions on Services Computing 12 (4):639–53. doi:10.1109/TSC.2016.2617331.
  • Liu, W., Y. He, X. Wang, Z. Duan, W. Liang, and Y. Liu. 2023. BFG: Privacy protection framework for internet of medical things based on blockchain and federated learning. Connection Science 35 (1):2199951. doi:10.1080/09540091.2023.2199951.
  • Liu, C., H. Li, Q. Zeng, T. Lu, C. Li, and C. Huang. 2020. Cross-organization emergency response process mining: An approach based on petri nets. Mathematical Problems in Engineering 2020:1–12. doi:10.1155/2020/8836007.
  • Liu, P., D. Wu, Z. Shen, and H. Wang. 2023. Trajectory privacy data publishing scheme based on local optimisation and R-tree. Connection Science 35 (1):2203880. doi:10.1080/09540091.2023.2203880.
  • Lu, Y., X. Huang, Y. Dai, S. Maharjan, and Y. Zhang. 2020. Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Transactions on Industrial Informatics 16 (6):4177–86. doi:10.1109/TII.2019.2942190.
  • Lv, Z., L. Qiao, M. S. Hossain, and B. J. Choi. 2021. Analysis of using blockchain to protect the privacy of drone big data. IEEE Network 35 (1):44–49. doi:10.1109/MNET.011.2000154.
  • Majeed, A., and S. Lee. 2021. Anonymization techniques for privacy preserving data publishing: A comprehensive survey. IEEE Access 9:8512–45. doi:10.1109/ACCESS.2020.3045700.
  • Mannhardt, F., S. Petersen, and M. Fradinho Duarte de Oliveira. 2019. A trust and privacy framework for smart manufacturing environments. Journal of Ambient Intelligence and Smart Environments 11 (3):201–19. doi:10.3233/AIS-190521.
  • Marin-Castro, H. M., and E. Tello-Leal. 2021. Event log preprocessing for process mining: A review. Applied Sciences 11 (22):10556. doi:10.3390/app112210556.
  • Nagy, Z., and A. Werner-Stark. 2022. An alignment-based multi-perspective online conformance checking technique. Acta Polytechnica Hungarica 19 (4):105–27. doi:10.12700/APH.19.4.2022.4.6.
  • Pika, A., M. T. Wynn, S. Budiono, A. H. M. Ter Hofstede, W. M. P. van der Aalst, and H. A. Reijers. 2020. Privacy-preserving process mining in healthcare. International Journal of Environmental Research and Public Health 17 (5):1612. doi:10.3390/ijerph17051612.
  • Raddatz, N., J. Coyne, P. Menard, and R. E. Crossler. 2021. Becoming a blockchain user: Understanding consumers’ benefits realisation to use blockchain-based applications. European Journal of Information Systems 32 (2):287–314. doi:10.1080/0960085X.2021.1944823.
  • Rafiei, M., and W. M. P. van der Aalst. 2021. Group-based privacy preservation techniques for process mining. Data & Knowledge Engineering 134:101908. doi:10.1016/j.datak.2021.101908.
  • Rösel, F., S. A. Fahrenkrog-Petersen, H. van der Aa, and M. Weidlich. 2021. A distance measure for privacy-preserving process mining based on feature learning. arXiv:2107.06578. http://arxiv.org/abs/2107.06578.
  • Shraga, R., A. Gal, D. Schumacher, A. Senderovich, and M. Weidlich. 2022. Process discovery with context-aware process trees. Information Systems 106:101533. doi:10.1016/j.is.2020.101533.
  • Singh, K. N., and A. K. Singh. 2022. Towards integrating image encryption with compression: A survey. ACM Transactions on Multimedia Computing, Communications and Applications 18 (3):1–21. doi:10.1145/3498342.
  • Torre, D., M. Alferez, G. Soltana, M. Sabetzadeh, and L. Briand. 2021. Modeling data protection and privacy: Application and experience with GDPR. Software and Systems Modeling 20 (6):2071–87. doi:10.1007/s10270-021-00935-5.
  • Usha Lawrance, J., and J. V. Nayahi Jesudhasan. 2021. Privacy preserving parallel clustering based anonymization for big data using MapReduce framework. Applied Artificial Intelligence 35 (15):1587–620. doi:10.1080/08839514.2021.1987709.
  • van der Aalst, W. 2012. Process mining: Overview and opportunities. ACM Transactions on Management Information Systems 3 (2):1–17. doi:10.1145/2229156.2229157.
  • van der Aalst, W., A. Adriansyah, A. K. A. de Medeiros, F. Arcieri, T. Baier, T. Blickle, J. C. Bose, P. van den Brand, R. Brandtjen, J. Buijs, et al. 2012. Process mining manifesto. In Business process management workshops, ed. F. Daniel, K. Barkaoui, and S. Dustdar, vol. 99, 169–94. Berlin Heidelberg: Springer. doi: 10.1007/978-3-642-28108-2_19.
  • Wen, Q., Y. Gao, Z. Chen, and D. Wu. 2019. A blockchain-based data sharing scheme in the supply chain by IIoT. 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), 695–700. doi: 10.1109/ICPHYS.2019.8780161.
  • Xu, Y., M. Z. A. Bhuiyan, T. Wang, X. Zhou, and A. K. Singh. 2023. C-FDRL: Context-aware privacy-preserving offloading through federated deep reinforcement learning in cloud-enabled IoT. IEEE Transactions on Industrial Informatics 19 (2):1155–64. doi:10.1109/TII.2022.3149335.
  • Yan, X., P. Yin, Y. Tang, and S. Feng. 2023. A remote sensing encrypted data search method based on a novel double-chain. Connection Science 35 (1):2165638. doi:10.1080/09540091.2023.2165638.
  • Yin, W. 2023. Zero-knowledge proof intelligent recommendation system to protect students’ data privacy in the digital age. Applied Artificial Intelligence 37 (1):2222495. doi:10.1080/08839514.2023.2222495.
  • Yogarajan, V., B. Pfahringer, and M. Mayo. 2020. A review of automatic end-to-end de-identification: Is high accuracy the only metric? Applied Artificial Intelligence 34 (3):251–69. doi:10.1080/08839514.2020.1718343.
  • Zhang, Y. 2023. Privacy-preserving with zero trust computational intelligent hybrid technique to English education model. Applied Artificial Intelligence 37 (1):2219560. doi:10.1080/08839514.2023.2219560.
  • Zhang, X., L. Qi, W. Dou, Q. He, C. Leckie, R. Kotagiri, and Z. Salcic. 2022. MRMondri-an: Scalable multidimensional anonymisation for big data privacy preservation. IEEE Transactions on Big Data 8 (1):125–39. doi:10.1109/TBDATA.2017.2787661.