Preserving source location privacy against the global attacker hiding in FOG

FOG computing provides real-time service for analyzing data sensed by massive wireless embedded devices. However, an attacker can get a global view of an event-driven sensor network through the contextual information collected in FOG. We present an attack model that uses such information to analyze traffic and infer the source location. To resist traffic analysis, dummy message injection approaches have been widely applied. In previous studies, a method in which all nodes in a global scope send packets periodically gains perfect privacy but fails to provide energy-efficient and low-latency service. We propose a novel scheme, DLSA (Dark-Light Stripe Alternation). Compared with state-of-the-art techniques, our methods can not only maintain perfect privacy but also reduce the communication overhead by more than 50% and the end-to-end latency by more than 40%.