Enabling Privacy-preserving Multidimensional Network Telemetry with Autoencoders
Yajie Zhou, Jason Li, Gianluca Stringhini, Ayse Kivilcim Coskun, Zaoxing Liu · 2023
Network telemetry systems are essential for monitoring network traffic and informing management decisions. However, increasing privacy concerns make user data access and analysis challenging for operators. We introduce PrvTel, a privacy-preserving telemetry system that uses an AutoEncoder model to encode user traffic data and preserve telemetry query ability with differential privacy guarantees. PrvTel features a lightweight model to be stored, facilitates quick training, and executes queries with minimal delay.