Exact In-Network Aggregation with Integrity and Confidentiality

In-network aggregation reduces the energy cost of processing aggregate queries (such as SUM, MAX, etc.) in wireless sensor networks. Recently, research has focused on secure in-network aggregation, motivated by the following two scenarios: 1) the sensors are deployed in open and unsafe environments, and 2) the aggregation process is outsourced to an untrustworthy service. Despite the bulk of work on the topic, there is currently no solution providing both integrity and confidentiality in the above scenarios. Moreover, existing solutions either return approximate results, or have limited applicability to certain types of aggregate queries. Our paper is the first work that provides both integrity and confidentiality in the aforementioned scenarios, while covering a wide range of aggregates and returning exact results. We initially present SIES, a scheme that solves exact SUM queries through a combination of homomorphic encryption and secret sharing. Subsequently, we show how to adapt SIES in order to support many other exact aggregate queries (such as MAX, MEDIAN, etc.). Finally, we augment our schemes with a functionality that identifies malicious sensors, preventing denial-of-service (DoS) attacks and attributing robustness to the system. Our techniques are lightweight and induce very small bandwidth consumption. Therefore, they constitute ideal solutions for resource-constrained sensors.