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Quantifying eavesdropping vulnerability in sensor networks
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Source ACM International Conference Proceeding Series; Vol. 96 archive
Proceedings of the 2nd international workshop on Data management for sensor networks table of contents
Trondheim, Norway
SESSION: Security table of contents
Pages: 3 - 9  
Year of Publication: 2005
ISBN:1-59593-206-2
Authors
Madhukar Anand  University of Pennsylvania
Zachary Ives  University of Pennsylvania
Insup Lee  University of Pennsylvania
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

With respect to security, sensor networks have a number of considerations that separate them from traditional distributed systems. First, sensor devices are typically vulnerable to physical compromise. Second, they have significant power and processing constraints. Third, the most critical security issue is protecting the (statistically derived) aggregate output of the system, even if individual nodes may be compromised. We suggest that these considerations merit a rethinking of traditional security techniques: rather than depending on the resilience of cryptographic techniques, in this paper we develop new techniques to tolerate compromised nodes and to even mislead an adversary. We present our initial work on probabilistically quantifying the security of sensor network protocols, with respect to sensor data distributions and network topologies. Beginning with a taxonomy of attacks based on an adversary's goals, we focus on how to evaluate the vulnerability of sensor network protocols to eavesdropping. Different topologies and aggregation functions provide different probabilistic guarantees about system security, and make different trade-offs in power and accuracy.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Madhukar Anand: colleagues
Zachary Ives: colleagues
Insup Lee: colleagues