Grayscale access control: Applying differential privacy to access control for Internet of Thing environment
Kangsoo Jung, Seog Park · 2017
As the essential role of data analysis in obtaining valuable information increases, the importance of acquiring useful data has also increased, especially within an Internet of Things (IoT) era. In this context, proper access control technique is required, because excessive data collection and analysis can lead to security breaches. However, existing access permission/denial binary access control techniques are not only unable to guarantee security, they can also significantly reduce information availability. In this paper, we propose a grayscale access control scheme that aims to minimize the trade-off between information usability and security by applying the differential privacy. We also propose a noise parameter determination algorithm that uses a Stackelberg game to determine the appropriate amount of noise insertion in differential privacy. We demonstrate that the proposed technique can determine the appropriate noise parameter values and achieve fine-grained access control through experiments.