Anonymized Network Sensing Graph Challenge
Hayden Jananthan, Michael Jones, William Arcand, David Bestor, William Bergeron, Daniel Burrill, Aydin Buluc, Chansup Byun, Timothy Davis, Vijay Gadepally, Daniel Grant, Michael Houle, Matthew Hubbell, Piotr Luszczek, Peter Michaleas, Lauren Milechin, Chasen Milner

TL;DR
This paper introduces the Anonymized Network Sensing Graph Challenge, aiming to foster community-driven solutions for analyzing anonymized network traffic data to enhance privacy-preserving network analysis.
Contribution
It presents a new challenge framework for developing innovative methods to construct and analyze anonymized traffic matrices from large-scale network data.
Findings
Utilizes over 100 billion network packets from CAIDA's Internet telescope.
Provides a reference implementation based on GraphBLAS.
Highlights novel approaches for privacy-preserving network analysis.
Abstract
The MIT/IEEE/Amazon GraphChallenge encourages community approaches to developing new solutions for analyzing graphs and sparse data derived from social media, sensor feeds, and scientific data to discover relationships between events as they unfold in the field. The anonymized network sensing Graph Challenge seeks to enable large, open, community-based approaches to protecting networks. Many large-scale networking problems can only be solved with community access to very broad data sets with the highest regard for privacy and strong community buy-in. Such approaches often require community-based data sharing. In the broader networking community (commercial, federal, and academia) anonymized source-to-destination traffic matrices with standard data sharing agreements have emerged as a data product that can meet many of these requirements. This challenge provides an opportunity to…
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Taxonomy
TopicsSecurity in Wireless Sensor Networks
