Special Session on Trustworthy and Responsible Data Mining (TRDM 2025)

Background

Data mining has become an integral tool in decision-making across diverse domains, including finance, healthcare, cybersecurity, and smart cities. However, as data mining becomes ubiquitous, it also raises various concerns related to fairness, interpretability, privacy, and security. Ensuring that data mining is trustworthy and responsible is critical to protect individuals and meet regulatory expectations from regulations such as GDPR and CCPA. This session on Trustworthy and Responsible Data mining hopes to bring together researchers and practitioners of data mining to discuss the aforementioned concerns about data mining and will provide unique insights into the latest research addressing them.

Aims and Scope

This special session aims to bring together researchers, practitioners, and policymakers to discuss advancements in responsible and ethical data mining. Topics of interest include, but are not limited to:

• Fairness-aware data mining techniques
• Transparency and interpretability in data mining
• Privacy-preserving data mining methods
• Secure multi-party computation for data mining
• Federated learning, differential privacy, homomorphic encryption
• Adversarial attacks and defenses in data mining
• Blockchain-based security mechanisms
• Case studies and practical applications of responsible data mining

Formatting Guidelines

Submissions should adhere to the following guidelines:

• Language: All papers must be written in English and present original, unpublished work.
• Format: Manuscripts should be prepared according to the Lecture Notes in Artificial Intelligence (LNAI) format. Templates and formatting details are available in Springer's Author Instructions.
• Length: Papers must not exceed 15 pages in LNAI format, including all content such as references, figures, and tables.
• Review Process: Submissions will undergo a double-blind peer review:
  • Author identities and affiliations must not be disclosed in the manuscript.
  • Authors should ensure that their manuscripts are anonymized and do not contain any identifying information.
• Originality: The submitted work must not be under consideration elsewhere and should contribute novel findings to the field of data mining.
• Supplementary Materials: If the paper includes supplementary materials, authors should ensure these do not reveal their identities and are submitted in a blinded form.

Submission Guidelines

Submissions must be made through the official conference submission system. Each submission will undergo a rigorous peer-review process by at least three reviewers. Accepted papers will be included in the conference proceedings and published in the Springer LNCS series.

More details on submission procedures can be found at ADMA 2025 Website.

Important Dates

• Paper Submission Deadline: May 8, 2025
• Notification of Acceptance: July 27, 2025
• Camera-Ready Submission: August 10, 2025
• Conference Dates: October 22-24, 2025

Organizing Team

• Dr. Chenxi Qiu (University of North Texas, USA) - Homepage
• Dr. Primal Pappachan (Portland State University, USA) - Homepage
• Dr. Li Yan (Xi’an Jiaotong University, China) - Homepage

Technical Program Committee

• Liuwang Kang, China Baidu Inc, Beijing, China
• Rongfei Zeng, Northeastern University, China
• Liangliang Su, Anhui Jianzhu University, China
• Bi Chongyang, Beijing Tsinghua Changgung Hospital, China
• Li Qiao, Beijing Tsinghua Changgung Hospital, China
• Hongbin Pei, Xi'an Jiaotong University, China