IEEE CSR SDGCP 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://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ieee-csr.org%2Fsdgcp%2F&data=05%7C02%7Cmusawp1%40di.unimi.it%7Ccfb53255704c4fa7070208dc8604ea63%7C13b55eef70184674a3d7cc0db06d545c%7C0%7C0%7C638532602921333713%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=91mORUZ8%2Fb8aDYwIcrXb3dDgBgK0zUURixrjk8VkHzM%3D&reserved=0 Submission portal: https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ieee-csr.org%2Fregistration%2F&data=05%7C02%7Cmusawp1%40di.unimi.it%7Ccfb53255704c4fa7070208dc8604ea63%7C13b55eef70184674a3d7cc0db06d545c%7C0%7C0%7C638532602921333713%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=A5tE00T7qDdZoGvqlM4bsViu%2FwsPs1xZWlV0FwfBH48%3D&reserved=0 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 30, 2024 AoE - Authors’ notification: July 14, 2024 AoE - Camera-ready submission: July 20, 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://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ieee-csr.org%2Fsdgcp%2F&data=05%7C02%7Cmusawp1%40di.unimi.it%7Ccfb53255704c4fa7070208dc8604ea63%7C13b55eef70184674a3d7cc0db06d545c%7C0%7C0%7C638532602921333713%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=91mORUZ8%2Fb8aDYwIcrXb3dDgBgK0zUURixrjk8VkHzM%3D&reserved=0 Organizers can be contacted at samira.maghool@unimi.it and f.a.bukhsh@utwente.nl . --- Filippo Berto Ph.D., Research Fellow Department of Computer Science, University of Milan filippo.berto@unimi.it *** La Statale per il futuro Salute, transizione digitale, sostenibilità Il tuo 5xmille ai nuovi progetti di ricerca dell'Università degli Studi di Milano Codice fiscale 80012650158