Some Important Aspects of Source Location Protection in Globally Attacked Sensor Networks

Details Some Important Aspects of Source Location Protection in Globally Attacked Sensor Networks Some Important Aspects of Source Location Protection in Globally Attacked Sensor Networks

2010-12-02

arxiv.org/abs/1012.0378v1

Computer Science

Cryptography and Security

longer version

Scientific paper

In the problem of location anonymity of the events exposed to a global eavesdropper, we highlight and analyze some aspects that are missing in the prior work, which is especially relevant for the quality of secure sensing in delay-intolerant applications monitoring rare and spatially sparse events, and deployed as large wireless sensor networks with single data collector. We propose an efficient scheme for generating fake network traffic to disguise the real event notification. The efficiency of the scheme that provides statistical source location anonymity is achieved by partitioning network nodes randomly into several dummy source groups. Members of the same group collectively emulate both temporal and spatial distribution of the event. Under such dummy-traffic framework of the source anonymity protection, we aim to better model the global eavesdropper, especially her way of using statistical tests to detect the real event, and to present the quality of the location protection as relative to the adversary's strength. In addition, our approach aims to reduce the per-event work spent to generate the fake traffic while, most importantly, providing a guaranteed latency in reporting the event. The latency is controlled by decoupling the routing from the fake-traffic schedule. A good dummy source group design also provides a robust protection of event bursts. This is achieved at the expense of the significant overhead as the number of dummy source groups must be increased to the reciprocal value of the false alarm parameter used in the statistical test. We believe that the proposed source anonymity protection strategy, and the evaluation framework, are well justified by the abundance of the applications that monitor a rare event with known temporal statistics, and uniform spatial distribution.

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