PI: Sencun Zhu (szhu@cse.psu.edu), Co-PI: Guohong Cao (gcao@cse.psu.edu)
As sensor networks scale in size, so will the amount of sensing data generated. The large volume of data coupled with the fact that the data are spread across the entire network creates a demand for efficient data dissemination/access techniques to find the relevant data from within the network. This demand has led to the development of data centric sensor (DCS) networks, where sensor data rather than sensor nodes are named based on attributes such as event type or geographic location. However, saving data in the network also creates critical security problems, which have not been addressed by previous work.
The objective of this research is to design secure DCS systems which provide two fundamental security services: data confidentiality and location privacy. Three types of attack models with increasing attacker capabilities are considered, including local passive attacks, sensor-assisted passive attacks, and compromise-based active attacks. Techniques based on the principle of least privilege, dummy traffic and filtering, location privacy and query optimization are designed to defend against the attacks.
The success of this research will have a much broader impact on making sensor networks more affordable and amenable to commercial, civilian, and military applications. It has the potential to foster new research in the privacy perspective of sensor networks. The results from this research will be disseminated widely through high quality publications and talks. The proposed research will also be integrated with the education curricula at Penn State.
