Special Track: Trustworthy Machine Learning for Web Information Systems

About:

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The special track “Trustworthy Machine Learning for Web Information Systems” aims to address the pivotal challenges and opportunities that arise at the intersection of trustworthy machine learning and web information systems. As machine learning becomes increasingly integral to web technologies, ensuring these systems are privacy-preserving, robust, secure, fair, and transparent is paramount. This track seeks to bring together a diverse array of research contributions that explore innovative approaches to enhancing the trustworthiness of machine learning based web information systems. Topics of interest include, but are not limited to, privacy enhancing technologies, robustness against adversarial attacks, fairness and bias mitigation, transparency and explainability in the context of web information systems. We expect to gather researchers from academia and industry to present the latest advances and future directions in designing, deploying secure and trustworthy machine learning algorithms, techniques, and protocols for real-world web applications, services, and systems. In this track, we solicit research papers and position papers to investigate best practices, new methods, and secure design principles. Ultimately, the goal is to advance the state of the art in creating machine learning based web systems that are not only technically proficient but also ethically sound, socially responsible, and trusted by users and stakeholders alike.

Organizers:

  • Xingliang Yuan (Lead), Associate Professor, The University of Melbourne, Australia
  • Helei Cui, Professor, Northwestern Polytechnical University, China
  • Yousra Aafer, Assistant Professor, University of Waterloo, Canada

Topics of Interest:

Areas of interest include (but not limited to):

  • Adversarial/poisoning threats against AI/ML based web applications and systems
  • Defences to improve deep learning web system robustness
  • Privacy-preserving machine learning algorithms and protocols for web applications and information systems
  • Privacy inference attacks against AI/ML based web systems, e.g., membership inference, model extraction, model inversion
  • Intersection among fairness, privacy, robustness, explainability, accountability, and environmental wellbeings in AI/ML powered web systems
  • Secure federated learning for decentralised and collaborative web applications and systems
  • Methodologies for detecting, measuring, and mitigating bias in machine learning models, with a focus on web applications
  • Ethical implications of deploying machine learning in web systems, including considerations of user consent, data protection, and societal impacts
  • Application of machine learning to detect, prevent, and respond to cyber threats in web systems
  • Studies focusing on the user perspective in ML powered web systems, including user studies, trust modeling, and user experience design
  • Development of benchmarks, datasets, and evaluation frameworks specifically designed for assessing the trustworthiness of machine learning in web information systems.

Submission Guidelines

Papers should be submitted in PDF format. The results described must be unpublished and must not be under review elsewhere. Submissions must conform to Springer’s LNCS format and should not exceed 15 pages, including all text, figures, references, and appendices. Submissions not conforming to the LNCS format, exceeding 15 pages, or being obviously out of the scope of the conference, will be rejected without review. Information about the Springer LNCS format can be found at Springer. Three to five keywords characterizing the paper should be indicated at the end of the abstract.

All submissions must go through EasyChair system via Easychair

Important Dates

  • Submission Deadline: 30 June, 2024 Extended 30 July 2024
  • Acceptance/Rejection Notification: 30 August, 2024
  • Camera-Ready Files Submission Deadline: 07 September, 2024

Publication

Please note that for every accepted paper, it is required that at least one person registers for the conference and presents the paper. All accepted papers will be included in the proceedings published as Springer’s LNCS series.