Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image Approach - Xianwen Fang; Mengyao Li (Applied Artificial Intelligence, 2024)
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.