Private, Secure, and Trust Data Analytics (PSTDA)

For more information, please visit the PSTDA special session’s webpage.

Welcome to the DSAA2023 Special Session on Private, Secure, and Trust Data Analytics (PSTDA2023). It is a special session of the 10th IEEE International Conference on Data Science and Advanced Analytics (DSAA2023).

The fusion of scalable computing infrastructure, big data, and artificial intelligence has boosted the development and application of data science and advanced data analytics. However, the recently emerging threats on the privacy, security, and trust (PST) of the data and the analytics models have shown a dramatically increasing trend with the wide deployment of data analytics applications.

This special session mainly focuses on the discussions of privacy, security, and trust in data analytics, which generally covers (but not limited to) the topics in privacy-preserving technology, privacy attacks, federated learning, machine unlearning, data poisoning attacks, model evasion attacks, adversarial learning, model robustness, secure machine learning integrating cryptographic techniques, blockchain techniques protection PST of data and models, etc. This special session invites authors to submit original research work that demonstrate and explore current advances in all related areas mentioned above. High-quality accepted papers will be recommended to the associated journal special issues.
  • Paper submission: May 22, 2023
  • Paper Notification: July 17, 2023
  • Paper Camera-ready: August 7, 2023
  • Amin Beheshti, Professor, Macquarie University
  • Victor S. Sheng, Associate professor, Texas Tech University
  • Guanfeng Liu, Senior Lecturer, Macquarie University