{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T07:30:19Z","timestamp":1771399819864,"version":"3.50.1"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study presents a Privacy-Preserving Process Data Generation method based on Dual-Discriminator Conditional Generative Adversarial Networks (P\n                      <jats:sup>3<\/jats:sup>\n                      DGAN) to generate privacy-preserving process data. To avoid mode collapse during model training, P\n                      <jats:sup>3<\/jats:sup>\n                      DGAN 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcomp.2026.1752739","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T06:56:00Z","timestamp":1771397760000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-preserving process data generation based on dual-discriminator conditional generative adversarial networks"],"prefix":"10.3389","volume":"8","author":[{"given":"Yi","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Tongji University, Key Laboratory of Embedded System and Service Computing, Ministry of Education","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Intelligent Science, Donghua University","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2699","DOI":"10.1007\/s10115-023-02042-x","article-title":"A systematic literature review on the application of process mining to industry 4.0","volume":"66","author":"Akhramovich","year":"2024","journal-title":"Knowl. 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