Lightweight privacy-preserving passive measurement for home networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Communications, ICC 2015
Number of pages6
ISBN (Electronic)9781467364324
StatePublished - Sep 9 2015
EventIEEE International Conference on Communications, ICC 2015 - London, United Kingdom
Duration: Jun 8 2015Jun 12 2015

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


ConferenceIEEE International Conference on Communications, ICC 2015
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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