Privacy-Preserving Pedestrian Detection for Smart City with Edge Computing

Edge computing is an ideal platform for pedestrian detection in smart city because of low latency and location awareness. In edge computing, data collected by IoT devices are processed on edge servers rather than being transported to cloud server. Compared with cloud computing, edge computing could avoid the possibility of pedestrians' privacy being leaked from cloud server or being stolen in the process of transmission. However, edge servers are not always safe. For instance, there are researches show that 89% of WiFi hotspots are unsecured. Hence, it is possible for attackers to know where you go at a given time of the day, which places you prefer to visit from images collected by IoT devices, such as camera, UAVs. Considering the data collected by IoT devices could include the sensitive information about users, we propose a scheme that applies differential privacy to protect the collected data. We experiment on the INRIA Person Dataset and use three deep learning networks. Results show that even though adding differential privacy makes images blurred, the deep learning network on edge servers can detect pedestrians in the images with accuracy as high as 97.3%.