Call for Paper

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 urgence, 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 train 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. This special session invites authors to submit original research work 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

Steering Committees

• Guanfeng Liu, Macquarie University, Australia 

• Xuyun Zhang, Macquarie University, Australia

General Chairs

• Lianyong Qi, Macquarie University, Australia

• Lina Yao, Data 61, CSIRO, Australia

• Shichao Pei, University of Massachusetts Boston, USA 

Program Chairs

• Pengpeng Zhao, Soochow University, China

• Tong Chen, The University of Queensland, Australia

• Xiyuan Hu, Nanjing University of Science and Technology, China

Important Dates

• Paper Submission: May 20, 2024

• Paper Notification: July 24, 2024

• Paper Camera-ready: August 21, 2024

Session time

• 4:00-6:00 pm (PDT) on Tuesday, October 8th.

Zoom meeting

• Join from a PC, Mac, iPad, iPhone or Android device:

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Paper presentation schedule

• 4:00-4:20 pm (PDT)

UPDATE: Mining User-News Engagement Patterns for Dual-Target Cross-Domain Fake News Detection (Xuankai Yang, Yan Wang, Xiuzhen Zhang, Shoujin Wang, Huaxiong Wang and Kwok-Yan Lam)

• 4:20-4:40 pm (PDT)

AddShare+: Efficient Selective Additive Secret Sharing Approach for Private Federated Learning (Bernard Atiemo Asare, Paula Branco, Iluju Kiringa and Tet Yeap)

• 4:40-5:00 pm (PDT)

SMP-Track: SAM in multi-pedestrian tracking (Shiyin Wang, Huafeng Liu, Qiong Wang, Yichao Zhou and Yazhou Yao)

• 5:00-5:20 pm (PDT)

Global-local unified cross-view enhancing framework for person re-identification (Tianran Chen, Yichao Zhou, Chen Chen and Xiyuan Hu)

• 5:20-5:40 pm (PDT)

Federated Feature Distillation CLIP for Photovoltaic Panels Defect Detection (Yiming Zhong and Min Cao)

• 5:40-6:00 pm (PDT)

Inter-Frame Multiscale Probabilistic Cross-Attention for Surveillance Object Detection (Huanhuan Xu, Xiyuan Hu and Yichao Zhou)

Paper Submission

All papers should be submitted electronically via EasyChair portal link: https://easychair.org/conferences/?conf=pstda2024 .

The length of each paper should be no more than ten (10) pages and should be formatted following the standard 2-column U.S. letter style of IEEE Conference template. For further information and instructions, see the IEEE Proceedings Author Guidelines.

All submissions will be blind reviewed by the Program Committee on the basis of technical quality, relevance to the conference's topics of interest, originality, significance, and clarity. Author names and affiliations 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.

Because of the double-blind review process, non-anonymous papers that have been issued as technical reports or similar cannot be considered for DSAA'2024. An exception to this rule applies to arXiv papers that were published in arXiv at least a month prior to DSAA'2024 submission deadline. Authors can submit these arXiv papers to DSAA provided that the submitted paper's title and abstract are different from the one appearing in arXiv.

Workshop Abstract 

With the exponential growth of mobile devices and the increasing demand for personalized and context-aware services, service-oriented applications play a crucial role in alleviating information overload and improving user experience. At the same time, Edge-Cloud Intelligence (ECI) provides a distributed computing paradigm that leverages the computational capabilities of edge servers to bring services closer to end-users, enabling low-latency and high-bandwidth data processing. The convergence of service applications and ECI has emerged as a promising way to enhance the efficiency and quality of service provisioning in today's dynamic and resource-constrained mobile environment. This call for papers invites researchers and practitioners to contribute to the exploration of the synergy between service applications and ECI infrastructure.

Potential Workshop Topics

The workshop invites original contributions in the form of research papers, case studies, and position papers, covering a broad range of topics related to edge-cloud intelligence. These topics may include, but are not limited to:

• Edge-cloud collaboration and coordination mechanisms

• Edge-cloud solutions for Internet of Things (IoT) applications

• Edge computing for multimedia processing and content delivery

• Edge-cloud intelligence for smart cities and urban computing

• Edge analytics and decision-making in healthcare and industry

• Edge-cloud intelligence for mobile and wireless networks

• Edge computing for autonomous systems and robotics

• Edge-cloud intelligence for natural language processing and recommendation systems

• Ethical, legal, and social implications of edge-cloud intelligence

Workshop Organizers

• Lianyong, Qi, China University of Petroleum (East China), China

• Xuyun, Zhang, Macquarie University, Australia

• Xiaolong, Xu, Nanjing University of Information Science and Technology, China

• Wenwen, Gong, Tsinghua University, China

Workshop Schedule/Important Dates

• Paper submission deadline: August 22, 2023

• Paper acceptance notification: September 15, 2023

• Workshop date: October 22, 2023