Prof. Dr. Adrian Perrig

aperrig

Prof. Dr. Adrian Perrig

CAB F 85.1
Universit├Ątstrasse 6
8092 Z├╝rich

Phone: +41 44 632 99 69
E-Mail: adrian.perrig@inf.ethz.ch



Publications

by Bartosz Przydatek, Dawn Song, Adrian Perrig
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.
Reference:
SIA: Secure Information Aggregation in Sensor Networks  [bibtex]Bartosz Przydatek, Dawn Song, Adrian Perrig. In Proceedings of ACM SenSys 2003.
Bibtex Entry:
@InProceedings{PrSoPe2003,
  author = 		 {Bartosz Przydatek and Dawn Song and Adrian Perrig},
  title = 		 {{SIA}: Secure Information Aggregation in Sensor Networks},
  url = {http://www.netsec.ethz.ch/publications/papers/sia.pdf},
  booktitle =	 {Proceedings of ACM SenSys},
  year =		 2003,
  month =		 nov,
  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.}
}