Antonios Michalas
About me
I received my PhD in Provable Security and Privacy from Aalborg University, Denmark and I currently work as an Associate Professor at the Department of Computing Sciences where I also lead the Network and Information Security group (NISEC). The group comprises PhD students, professors a nd researchers. Group members conduct research in areas spanning from the theoretical foundations of cryptography to the design and implementation of leading-edge efficient and secure communication protocols. Apart from my research work at NISEC, as an associate professor, I am actively involved in the teaching activities of the University. Finally, my role expands to student supervision and research project coordination.
You can find more information on my profile and my latest activities at www.amichalas.com
Fields of expertise
- Applied Cryptography;
- Operations on Encrypted Data;
- Privacy Enhancing Technologies;
- Privacy-Preserving Machine Learning;
- and...anything that looks interesting!
Latest publications
Blind Brother: Attribute-Based Selective Video Encryption
Frimpong, E., Liu, B., Nuoskala, C. & Michalas, A., 4 Jun 2025, Proceedings of the 15th ACM Conference on Data and Application Security and Privacy: CODASPY '25. ACM, p. 371-382Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Challenges and Solutions in Internet of Things-Based Smart Applications
Shekokar, N. M., Shinde, S. K., Ambarkar, S. S., Michalas, A., Mangla, M. & Thomas, A., Jan 2025, Chapman and Hall/CRC. 186 p.Research output: Book/Report › Anthology › Scientific › peer-review
FaaS and Furious: Accelerating Privacy-Preserving ML with Function as a Service at the Edge
Tusa, F., Michalas, A., Bowden, J. & Kiss, T., 2025, 2025 34th International Conference on Computer Communications and Networks (ICCCN). IEEE, p. 1-6 (Proceedings : International Conference on Computer Communications and Networks).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
To Vaccinate or Not to Vaccinate? Analyzing X Power over the Pandemic
Khan, T., Sohrab, F., Michalas, A. & Gabbouj, M., 2025, Advanced Information Networking and Applications: Proceedings of the 39th International Conference on Advanced Information Networking and Applications (AINA-2025), Volume 7. Springer, p. 352-365 14 p. (Lecture Notes on Data Engineering and Communications Technologies; vol. 251).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
A More Secure Split: Enhancing the Security of Privacy-Preserving Split Learning
Khan, T., Nguyen, K. & Michalas, A., 2024, Secure IT Systems: 28th Nordic Conference, NordSec 2023, Oslo, Norway, November 16–17, 2023, Proceedings. Fritsch, L., Hassan, I. & Paintsil, E. (eds.). Cham: Springer, p. 307-329 23 p. (Lecture Notes in Computer Science; vol. 14324).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
A Pervasive, Efficient and Private Future: Realizing Privacy-Preserving Machine Learning Through Hybrid Homomorphic Encryption
Nguyen, K., Budzys, M., Frimpong, E., Khan, T. & Michalas, A., 5 Nov 2024, 2024 IEEE Conference on Dependable, Autonomic and Secure Computing (DASC). IEEE, p. 47-56 10 p. (IEEE International Conference on Dependable, Autonomic and Secure Computing).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
FE[r]Chain: Enforcing Fairness in Blockchain Data Exchanges Through Verifiable Functional Encryption
Nuoskala, C., Rabbaninejad, R., Dimitriou, T. & Michalas, A., Jun 2024, Proceedings of the 29th ACM Symposium on Access Control Models and Technologies (SACMAT 2024). ACM, p. 183-191 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption
Frimpong, E., Nguyen, K., Budzys, M., Khan, T. & Michalas, A., 2024, Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (SAC '24). ACM, p. 953-962 10 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation
Khan, T. & Michalas, A., 2024, Proceedings - 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom/BigDataSE/CSE/EUC/iSCI 2023. Hu, J., Min, G. & Wang, G. (eds.). IEEE, p. 62-71 10 p. (IEEE International Conference on Trust, Security and Privacy in Computing and Communications).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learning
Khan, T., Budzys, M. & Michalas, A., 24 Jun 2024, Proceedings of the 29th ACM Symposium on Access Control Models and Technologies (SACMAT 2024). ACM, p. 19-30 12 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review