Special Session Aims and Scope

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. Specifically, the PST attacks on data or models such as model inversion attacks, membership inference attacks, data poisoning attacks, evasion attacks, and model backdoors, have severely made advanced data analytics highly vulnerable, particularly in common scenarios where data are distributed or computation is outsourced like MLaaS (Machine Learning as a Service). On the other hand, defence solutions are proposed as new computing schemes, PST frameworks, algorithms, and methods. For example, differential privacy, federated learning, and machine unlearning are proposed for privacy protection in data analytics, and adversarial machine learning is proposed to achieve robust, secure, and trustworthy data analytics. Given the importance and urgency, this special issue aims to provide a venue for researchers, practitioners and developers from different background areas relevant to PST and data analytics to exchange their latest experience, research ideas, and synergic research and development on fundamental issues and applications about privacy, security, and trust issues in data analytics, as a strong supplement to the main track of data science and advanced analytics.

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.

Topics of Interest

This special session invites authors to submit original manuscripts that demonstrate and explore current advances in all related areas mentioned above. Topics of interest include, but are not limited to:

  • New privacy, security and trust opportunities and challenges in data analytics

  • Novel theories and modelling for privacy, security, and trust in data analytics

  • Private, secure, and trust deep learning for data analytics

  • Privacy-preserving data mining and machine learning

  • Federated/collaborative learning

  • Machine unlearning

  • Adversarial machine learning for robust data analytics

  • Transfer learning for private, secure, and trust data analytics

  • Data poisoning and model evasion attacks and defences

  • Cryptographic techniques based private, secure, and trust data analytics

  • Privacy, security, and trust management for data analytics

  • Blockchain for privacy, security, and trust in data analytics

  • Real-world applications for private, secure and trust data analytics


Submission Guideline and Reviewing

Submission portal: https://cmt3.research.microsoft.com/DSAA2022

Special session papers strictly follow the same specifications, requirements, and policies as the main conference submissions in terms of paper formatting and length and important policies. Reviewing the submissions in each special session is coordinated by the special session organizers and is fully aligned to the main conference evaluation process. See [DSAA2022 Important Policies](http://dsaa2022.dsaa.co/research-application-track-and-journal-track-papers/) for more details. In particular:

  • All papers submitted to special sessions will be double-blind reviewed by the Program Committee on the basis of technical quality, relevance to the scope of the special session, originality, significance, and clarity. The names and affiliations of authors must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with paper formatting and authors anonymity will be rejected without reviews.

  • Submissions must be original work and should not be under submission to other venues at the time of review.

  • The length of each paper submitted to the special session should be no more than 10 pages, and the papers should be formatted following the standard 2-column U.S. letter style of IEEE Conference template. See the IEEE Proceedings Author Guidelines: http://www.ieee.org/conferences_events/conferences/publishing/templates.html, for further information and instructions.

  • Authors are also encouraged to submit supplementary materials, i.e., providing the source code and data through a GitHub-like public repository to support the reproducibility of their research results.

  • Papers will then be assigned to appropriate PCMs by the Special Session organizers for review.

  • Special Session organizers will make recommendations of acceptance/rejection for papers in their sessions, which must be validated by General chairs, Research, and Application track chairs.

  • To guarantee uniform quality control for all special sessions and to be consistent with the main conference, the final decisions of special session paper acceptance/rejection are made by the DSAA Program Chairs.

Proceedings, Indexing and Special Issues

All accepted full-length special session papers will be published by IEEE in the DSAA main conference proceedings under its Special Session scheme. All papers will be submitted for inclusion in the IEEEXplore Digital Library. The conference proceedings will be submitted for EI indexing through INSPEC by IEEE. At least one of the authors of the accepted papers must register for the special session for the paper to be included into the conference proceedings. The accepted papers will be invited for presentation during the conference. High quality accepted workshop papers with significant revision and extension would be further recommended to special issues in the following associated SCI/SCIE indexed journals.
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