Secure and Privacy-Aware Data Collection in Wireless Sensor Networks
Abstract: A wireless sensor network is a collection of numerous sensors distributed on an area of interest to collect and process data from the environment. One particular threat in wireless sensor networks is node compromise attacks, that is, attacks where the adversary gets physical access to a node and to the programs and keying material stored on it.Only authorized queries should be allowed in the network and the integrity and confidentiality of the data that is being collected should be protected. We propose a layered key distribution scheme together with two protocols for query authentication and confidential data aggregation. The layered key distribution is more robust to node and communication failures than a predefined tree structure. The protocols are secure under the assumption that less than n sensor nodes are compromised. n is a design parameter that allows us to trade off security for overhead. When more than n sensor nodes are compromised, our simulations show that the attacker can only introduce unauthorized queries into a limited part of the network and can only get access to a small part of the data that is aggregated in the network. Considering the data collection protocol we also contribute with strategies to reduce the energy consumption of an integrity preserving in-network aggregation scheme to a level below the energy consumption of a non-aggregation scheme. Our improvements reduce node congestion by a factor of three and the total communication load by 30%.Location privacy of the users carrying mobile devices is another aspect considered in this thesis. Considering a mobile sink that collects data from the network, we propose a strategy for data collection that requires no information about the location and movement pattern of the sink. We show that it is possible to provide data collection services, while protecting the location privacy of the sink. When mobile phones with built-in sensors are used as sensor nodes, location information about where the data has been sensed can be used to trace users and infer other personal information about them, like state of health or personal preferences. Therefore, location privacy preserving mechanisms have been proposed to provide location privacy to the users. We investigate how a location privacy preserving mechanism influences the quality of the collected data and consider strategies to reconstruct the data distribution without compromising location privacy.
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