pDCS: Security and Privacy Support for Data-Centric Sensor Networks

Abstract

The demand for efficient data dissemination/access techniques to find the relevant data from within a sensor network has led to the development of data-centric sensor networks (DCS), where the sensor data as contrast to sensor nodes are named based on attributes such as event type or geographic location. However, saving data inside a network also creates security problems due to the lack of tamper-resistance of the sensor nodes and the unattended nature of the sensor network. For example, an attacker may simply locate and compromise the node storing the event of his interest. To address these security problems, we present pDCS, a privacy-enhanced DCS network which offers different levels of data privacy based on different cryptographic keys. In addition, we propose several query optimization techniques based on Euclidean Steiner Tree and Keyed Bloom Filter to minimize the query overhead while providing certain query privacy. Finally, detailed analysis and simulations show that the Keyed Bloom Filter scheme can significantly reduce the message overhead with the same level of query delay and maintain a very high level of query privacy.