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Ridesharing services, such as Uber and Didi, are enjoying great popularity; however, a big challenge remains in guaranteeing the safety of passenger and driver. State-of-the-art work has primarily adopted the cloud model, where data collected through end devices on vehicles are uploaded to and processed in the cloud. However, data such as video can be too large to be uploaded onto the cloud in real time. When a vehicle is moving, the network communication can become unstable, leading to high latency for data uploading. In addition, the cost of huge data transfer and storage is a big concern from a business point of view. As edge computing enables more powerful computing end devices, it is possible to design a latency-guaranteed framework to ensure in-vehicle safety. In this paper, we propose an edge-based attack detection in ridesharing services, namely SafeShareRide, which can detect dangerous …