Differentially-private big data analytics for high-speed research network traffic measurement

High-speed research networks (e.g., Internet2, Géant) represent the backbone of large-scale research projects that bring together stakeholders from academia, industry and government. Such projects have increasing demands on throughput (e.g., 100Gbps line rates), and require a high amount of configurability. Collecting and sharing traffic data for such networks can help in detecting hotspots, troubleshooting, and designing novel routing protocols. However, sharing network data directly introduces serious privacy breaches, as an adversary may be able to derive private details about individual users (e.g., personal preferences or activity patterns). Our objective is to sanitize high-speed research network data according to the de-facto standard of differential privacy (DP), thus supporting benefic applications of traffic measurement without compromising individuals' privacy. In this paper, we present an initial framework for computing DP-compliant big data analytics for high-speed research network data. Specifically, we focus on sharing data at flow-level granularity, and we describe our initial steps towards an environment that relies on Hadoop and HBase to support privacy-preserving NetFlow analytics.