[Storage-research-list] CFP: IEEE CSR SDG (Submission Deadline: June 3nd, 2024)

[Filippo Berto]

IEEE CSR SDG Workshop
2024 IEEE International Conference on Cyber Security and Resilience 
Workshop on Synthetic Data Generation for a Cyber-Physical World
++++++++++++++++++++++++++
2 - 4 September 2024
* London * UK * Hybrid event *
Workshop: https://www.ieee-csr.org/sdg/
Submission portal: https://www.ieee-csr.org/registration/


The Synthetic Data Generation workshop of the IEEE Cyber Security and 
Resilience conference, ai its first edition, is the event that aims to 
put together Data Science researchers and professionals from academia, 
industry, government, and public administration working in the field of 
big data and data science, as well as related fields (e.g., security and 
privacy, HPC, Cloud). This workshop aims to address the need for 
replicable and safe sharing of information by promoting the advancement 
of our community towards synthetic data generation. By creating 
synthetic datasets that mimic real-world phenomena, researchers can 
effectively overcome barriers associated with limited access to 
sensitive or proprietary data. In doing so, we not only foster 
interdisciplinary collaborations but also accelerate scientific discovery.


## Brief Description

Synthetic datasets that reflect the statistical properties of authentic 
data allow us to share research insights and findings without 
compromising privacy or proprietary interests. This approach not only 
promotes transparency and reproducibility in research but also 
encourages interdisciplinary collaboration and knowledge sharing. 
Artificial Intelligence (AI) is one of the main areas utilizing 
generated synthetic data. Privacy issues arise once the dataset contains 
sensitive features playing a role in training AI systems. Given the high 
cost and time-consuming nature of data collection, as well as the 
potential for shortcomings such as low data volume, non-compliance with 
regulations, and bias, there is a risk of not only achieving biased and 
low-performance models but also violating privacy principles. Synthetic 
data generation can facilitate analysis, the need for data augmentation, 
or prevent data breaches in highly sensitive domains, rather than weak 
anonymization approaches. Generative Adversarial Networks (GAN), 
Variational Autoencoders (VAE), and Agent-based modeling (ABM) are among 
the most common synthetic data generation algorithms.


However, it is critical to recognize the limitations of synthetic data 
generation, particularly in capturing the intricacies and 
interdependencies present in real-world systems. While synthetic 
datasets can mimic statistical distributions and patterns, they may 
struggle to replicate the nuanced relationships and contextual nuances 
inherent in complex phenomena. By leveraging advances in artificial 
intelligence, machine learning, and computational modeling, researchers 
can strive to bridge the gap between synthetic and authentic data, 
unlocking new opportunities for insight and innovation in fields as 
diverse as healthcare, finance, social sciences, and beyond.


## Topics of Interest

Prospective authors are encouraged to submit previously unpublished 
contributions from a broad range of topics, which include but are not 
limited to the following:

- Privacy-preserving in healthcare data
- Algorithms for debiasing datasets (in the pre-processing phase of ML 
modeling)
- Algorithms for debiasing the ML models’ results
- Uncovering and mitigating synthetic data algorithmic bias
- Assurance and certification of the dataset and ML models
- Synergy of ABM with ML focusing on the rule extraction
- Domain-dependent/independent synthetic data generation challenges and 
opportunities
- FAIR (findability, accessibility, interoperability, and reuse) and 
ethical synthetic data generation
- Explainability and interpretability aspects in synthetic data generation


## Important Dates

- Paper submission deadline: June 3, 2024 AoE
- Authors’ notification: July 3, 2024 AoE
- Camera-ready submission: July 14, 2024 AoE
- Early registration deadline: July 20, 2024 AoE
- Workshop date: September 2-4, 2024


## Workshop Chairs

- Samira Maghool, Department of Computer Science, Universita' degli 
Studi di Milano
- Faiza Allah Bukhsh, Faculty of Electrical Engineering, Mathematics and 
Computer Science, University of Twente


## Organizing Commitee

- Ernesto Damiani, Department of Computer Science, Khalifa University
- Paolo Caravolo, Department of Computer Science, Universita' degli 
Studi di Milano
- Samira Maghool, Department of Computer Science, Universita' degli 
Studi di Milano
- Faiza Allah Bukhsh, Faculty of Electrical Engineering, Mathematics and 
Computer Science, University of Twente


## Technical Program Committee

- Mirela Riveni, University of Groningen
- Juba Agoun, Universite` Lumie're Lyon 2
- Valerio Bellandi, Universita' degli Studi di Milano
- Nicola Bena, Universita' degli Studi di Milano
- Filippo Berto, Universita' degli Studi di Milano
- Afshin Montakhab Shiraz, University
- Marco Cremonini, Universita' degli Studi di Milano
- Elena Casiraghi, Universita' degli Studi di Milano
- Rob Bemthuis, University of Twente
- Sanja Lazarova-Molnar Karlsruhe Institute of Technology
- Azzam Mourad Lebanese American, University
- Anastasija Nikiforova, University of Tartu
- MohammadReza Fani Sani Microsoft
- Paolo Caravolo, Universita' degli Studi di Milano
- Maya Daneva, University of Twente
- Jeewanie Jayasinghe Arachchig, University of Twente
- Rabia Maqsood National, University of Computer and Emerging Sciences 
CHINIOT-FAISALABAD CAMPUS
- Marco Angelini, University of Rome "La Sapienza"
- Alessandro Palma, University of Rome "La Sapienza"
- Robert Wrembel Poznan, University of Technology, Computer Science
- Ehsan Ullah Munir, Comsats University


Please don't hesitate to ask further questions.

This call for papers and additional information about the conference can 
be found at https://www.ieee-csr.org/sdg/

Organizers can be contacted at samira.maghool at unimi.it and 
f.a.bukhsh at utwente.nl .

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Filippo Berto Ph.D., Research Fellow
Department of Computer Science, University of Milan
filippo.berto at unimi.it