Lightweight privacy-preserving passive measurement for home networks

Homes now constitute a significant fraction of the Internet´s “edge”. Despite a number of recent efforts, hard data about the structure and use of home networks is still hard to come by. In particular, data sets that include information about the traffic going into and out of homes tend to include very limited numbers of endpoints. Two of the main challenges in collecting such information are: (i) the computational and storage requirements of passive measurement systems, relative to the limited capabilities of home routers; and (ii) individuals´ concerns about the privacy of their traffic data. In this paper we introduce HNFL, a lightweight, privacy-preserving passive measurement infrastructure for home networks. HNFL provides a lightweight network flow data collector in Linux kernel, which presents flow data in the form of bipartite graphs that support both latitudinal and longitudinal studies and a scalable and irreversible method to hide traffic identities from flow data while maintaining longitudinal comparison. We evaluate the correctness and efficiency of HNFL, and explore some applications for both networking researchers and home network users.