Mihail is a security researcher and a PhD student at the University of Bucharest. His area of research is applied cryptography with a focus on privacy-preserving machine learning. Mihail has authored and co-authored various research papers on topics ranging from quantum cryptography to confidential computing in cybersecurity.

The best of both worlds: privacy and security

Nowadays we see more and more cybersecurity solutions in the cloud. From endpoint to network security, cloud-based solutions are more easily managed and versatile. Unfortunately, the flexibility of the cloud comes with a major downside: privacy. This type of architecture forces you to trade between security and privacy. In this talk, we will discuss various privacy-preserving technologies for confidential cybersecurity. We will see private versions of some of the most used anomaly detection algorithms that are used in cybersecurity solutions. Specifically, we will take a tour of different libraries like TensorFlow Privacy or OpenFHE and we will discuss how to easily implement privacy-preserving anomaly detection for cybersecurity use cases.