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 privacypreserving 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
  • Privacy, security and privacy issues, trends, and challenges in data analytics

Submission Guideline and Reviewing

Submission Instructions:

  • Step 1: Login and enter DSAA conference in EasyChair. Website: https://easychair.org/conferences/?conf=dsaa2023
  • Step 2: Select your role as "author". From the top menu, click the "New Submission" button, and then select "Special Session: Private, Secure, and Trust Data Analytics" to continue.
  • Step 3: Enter your paper information and then use the "Submit" button at the bottom of the form.

Paper Length, Formatting, and Reviewing:

  • The length of each paper submitted should be no more than 10 pages, and formatted following the standard 2-column U.S. letter style of IEEE Conference template. See the IEEE Proceedings Author Guidelines for further information and instructions.
  • All submissions 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.
  • 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.

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.

Important Policies

Reproducibility: The advancement of science depends heavily on reproducibility. We strongly recommend that the authors release their code and data to the public. Authors can provide an optional two-page supplement at the end of their submitted paper (it needs to be in the same PDF file and start at page 11). This supplement can only be used to include:
  • Information necessary for reproducing the experimental results reported in the paper (e.g., various algorithmic and model parameters and configurations, hyper parameter search spaces, details related to data set filtering and train/test splits, software versions, detailed hardware configuration, etc.).
  • Any data, pseudo-code and proofs that could not be included in the main page of the manuscript due to space limitations.
Authorship: The list of authors at the time of submission is considered final and any further changes of the authorship are not allowed.

Dual Submissions: DSAA is an archival publication venue as such submissions that have been previously published, accepted, or are currently under consideration at other peer-review publication venues (i.e., journals, conferences, workshops with published proceedings, etc) are not permitted.

Conflicts of Interest (COI): COIs must be declared at the time of submission. COIs include employment at the same institution within the past three years, collaborations during the past three years, advisor/advisee relationships, plus family and close friends.

Attendance: At least one of the authors of each accepted paper must register in full and attend the conference to present the paper. No-show papers will be removed from the IEEE Xplore proceedings.