Date of Original Version
Abstract or Description
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.