Auditing images collected by sensors in ambient intelligence systems with privacy and high efficiency

To determine whether sensors should transmit collected images back to the data center, an auditing protocol is desired to check these images before transmission. Since images may contain sensitive information of the environment, privacy is an essential requirement for such an auditing protocol. Moreover, since sensors are typically low-power devices and the data center has to handle a variety of sensors, this auditing protocol should be lightweight. Taking both security and efficiency into account, we propose a novel auditing protocol on images in ambient intelligence systems, called AP-AmI (i.e., Auditing Protocol in Ambient Intelligence Systems). In AP-AmI, we use the local binary pattern technique for extracting features from images and design a novel privacy-preserving Euclid distance computation algorithm for determining whether these collected images are useful. Since these two techniques are both lightweight, AP-AmI can achieve high efficiency. At the same time, since feature vectors cannot be extracted by adversaries, AP-AmI can satisfy the privacy requirement. Experimental results show AP-AmI is feasible for real-world applications.