Privacy-preserving process data generation based on dual-discriminator conditional generative adversarial networks

IntroductionThe growing adoption of data-centric business analytics demands effective safeguarding techniques for processing data that contains procedural details. Although Petri net-driven process mining successfully extracts operational knowledge from activity sequences, current protection approaches often diminish analytical value. Therefore, preserving process-related information while ensuring privacy remains a critical challenge.MethodsThis study presents a Privacy-Preserving Process Data Generation method based on Dual-Discriminator Conditional Generative Adversarial Networks (P3DGAN) to generate privacy-preserving process data. To avoid mode collapse during model training, P3DGAN employs two discriminators that separately model the dataflow and workflow characteristics of process data. Furthermore, we propose a game-optimization strategy based on Petri net theory to capture the global distribution characteristics of process data. Furthermore, we introduce a workflow-level privacy metric based on the Euclidean distance between trace variants (ED-TV) to support comprehensive risk assessment.ResultsExperimental results on four real-world process datasets demonstrate that our method can generate high-quality process data with strong privacy protection compared with competitive peers.DiscussionThe proposed framework achieves an effective multi-dimensional privacy-utility trade-off, demonstrating its potential for practical applications in privacy-sensitive domains such as healthcare, banking, and manufacturing.