The research of SIDS is at the intersection of three areas: cybersecurity, machine learning, and distributed systems.

Our current research directions are:

• Security of machine learning. Our focus has been on assuring confidentiality of sensitive data while performing inference with neural networks. Examples:
  • D. Chabal, T. Muller, E. Zhang, D. Sapra, C. d. Laat, Z. Á. Mann. COLIBRI: Optimizing Multi-Party Secure Neural Network Inference Time for Transformers. 40th IFIP International Conference on ICT Systems Security and Privacy Protection (IFIP SEC), pp. 17-31, 2025. https://link.springer.com/chapter/10.1007/978-3-031-92882-6_2
  • E. Zhang, Z. Á. Mann. Predicting the execution time of secure neural network inference. IFIP International Conference on ICT Systems Security and Privacy Protection (SEC), pp. 481-494, 2024. https://link.springer.com/chapter/10.1007/978-3-031-65175-5_34  
  • Z. Á. Mann, C. Weinert, D. Chabal, J. W. Bos. Towards Practical Secure  Neural Network Inference: The Journey So Far and the Road Ahead. ACM  Computing Surveys, 56(5): Article 117, 2023. https://doi.org/10.1145/3628446  
• Automated management of IT security risks. We develop methods to automatically assess security risks and mitigation actions. Examples:
  • Z. Á. Mann. Time is money: A temporal model of cybersecurity. 40th IFIP International Conference on ICT Systems Security and Privacy Protection (IFIP SEC), pp. 82-96, 2025. https://link.springer.com/chapter/10.1007/978-3-031-92886-4_6
  • Z. Á. Mann. Urgency in cybersecurity risk management: toward a solid theory. IEEE 37th Computer Security Foundations Symposium (CSF), pp. 651-664, 2024. https://ieeexplore.ieee.org/document/10664345
  • S. S. Zmiewski, J. Laufer, Z. Á. Mann. Automatic online quantification  and prioritization of data protection risks. 17th International  Conference on Availability, Reliability and Security (ARES), Article 7,  2022. https://doi.org/10.1145/3538969.3539005 
• Privacy-enhancing technologies. Our aim is to support the optimal choice and configuration of privacy-enhancing technologies during system design and during operations. Examples:
  • A. Al-Momani, D. Balenson, C. Bösch, Z. Á. Mann, S. Pape, J. Petit. Lessons from a robotaxi: Challenges in selecting privacy-enhancing technologies. Computer Security - ESORICS 2025 International Workshops, pp. 154-170, 2026. https://link.springer.com/chapter/10.1007/978-3-032-16089-8_11 
  • Z. Á. Mann, J. Petit, S. M. Thornton, M. Buchholz, J. Millar. SPIDER: Interplay Assessment Method for Privacy and Other Values. IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), pp. 1-8, 2024. https://ieeexplore.ieee.org/abstract/document/10628815 
  • D. Ayed, P.-A. Dragan, E. Félix, Z. Á. Mann, E. Salant, R. Seidl, A. Sidiropoulos, S. Taylor, R. Vitorino. Protecting sensitive data in the cloud-to-edge continuum: The FogProtect approach. 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 279-288, 2022. https://ieeexplore.ieee.org/abstract/document/9826058