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SIA: secure information aggregation in sensor networks

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Conference On Embedded Networked Sensor Systems archive
Proceedings of the 1st international conference on Embedded networked sensor systems table of contents

Los Angeles, California, USA

SESSION: Compression & aggregation table of contents

Pages: 255 - 265

Year of Publication: 2003

ISBN:1-58113-707-9

Authors

Bartosz Przydatek	Carnegie Mellon University, Pittsburgh, PA
Dawn Song	Carnegie Mellon University, Pittsburgh, PA
Adrian Perrig	Carnegie Mellon University, Pittsburgh, PA

Sponsors

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
ACM: Association for Computing Machinery
SIGCOMM: ACM Special Interest Group on Data Communication
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGOPS: ACM Special Interest Group on Operating Systems

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 22, Downloads (12 Months): 159, Citation Count: 73

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ABSTRACT

Sensor networks promise viable solutions to many monitoring problems. However, the practical deployment of sensor networks faces many challenges imposed by real-world demands. Sensor nodes often have limited computation and communication resources and battery power. Moreover, in many applications sensors are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the sensor's cryptographic keys.One of the basic and indispensable functionalities of sensor networks is the ability to answer queries over the data acquired by the sensors. The resource constraints and security issues make designing mechanisms for information aggregation in large sensor networks particularly challenging.In this paper, we propose a novel framework for secure information aggregation in large sensor networks. In our framework certain nodes in the sensor network, called aggregators, help aggregating information requested by a query, which substantially reduces the communication overhead. By constructing efficient random sampling mechanisms and interactive proofs, we enable the user to verify that the answer given by the aggregator is a good approximation of the true value even when the aggregator and a fraction of the sensor nodes are corrupted. In particular, we present efficient protocols for secure computation of the median and the average of the measurements, for the estimation of the network size, and for finding the minimum and maximum sensor reading. Our protocols require only sublinear communication between the aggregator and the user. To the best of our knowledge, this paper is the first on secure information aggregation in sensor networks that can handle a malicious aggregator and sensor nodes.

REFERENCES

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