Abstract: Data privacy is an important aspect to be dealt with during data transmission, data query and data storage in Wireless Sensor Networks (WSNs). Issue of privacy of the data collected and transmitted from the sensor nodes in wireless sensor networks is also of major concern. The salient features like uncontrollable environment, sensor node resource constraints and topological constraints have to be taken into consideration to have a better tradeoff among privacy, accuracy and power consumption. In this study, a new scheme, Hierarchical based Multidimensional Data Perturbation (HMDP) is proposed and Geometric Data Perturbation (GDP) is suggested for perturbing the data collected by the sensor nodes during data transmission. GDP perturbs the data randomly while broadcasting the data collected from the sensor nodes to the central processing server or another sensor node. A comparative study of the proposed method with the existing methods is presented. The proposed technique gives a better tradeoff in terms of the metrics accuracy, privacy and power consumption.
K. Sreekumar and E. Baburaj, 2016. Privacy-Preserving Data Transmission Using Geometric Data Perturbation in Wireless Sensor Networks. Asian Journal of Information Technology, 15: 2447-2456.