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Designing router scheduling policies: a privacy perspective

Published: 04 October 2010 Publication History

Abstract

We examine a queuing side channel which results from a shared resource between two users in the context of packet networks. We consider the scenario where one of them is a legitimate user and the other is an attacker who is trying to learn about the former's activities. We show that the waiting time of an adversary sending a small but frequent probe stream to the shared resource (e.g., a router) is highly correlated with traffic pattern of the user.
Through precise modeling of the constituent flows and the scheduling policy of the shared resource, we describe a dynamic program to compute the optimal privacy preserving policy that minimizes the correlation between user's traffic and attacker's waiting times. While the explosion of state-space for the problem prohibits us from characterizing the optimal policy, we derive a sub-optimal policy using a myopic approximation to the problem. Through simulation results, we show that indeed the sub-optimal policy does very well in high traffic regime. Furthermore, we compare the privacy/delay trade-offs among various scheduling policies, some already widely deployed in scheduling and others suggested by us based on the intuition from the myopic approximation.

Reference

[1]
]]X. Gong, N. Kiyavash and N. Borisov, Fingerprinting Websites Using Remote Traffic Analysis, in preparation for conference submission, Available online at http://www.ifp.illinois.edu/~kadloor1/attackdescription.pdf.

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      cover image ACM Conferences
      CCS '10: Proceedings of the 17th ACM conference on Computer and communications security
      October 2010
      782 pages
      ISBN:9781450302456
      DOI:10.1145/1866307

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 04 October 2010

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      Author Tags

      1. network forensics
      2. side channels
      3. traffic analysis

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      CCS '10 Paper Acceptance Rate 55 of 325 submissions, 17%;
      Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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