  Personal monitoring devices such as cyclist helmet cameras to record
accidents or dash cams to catch collisions have proliferated, with more
companies producing smaller and compact recording gadgets. As these devices are
becoming a part of citizens' everyday arsenal, concerns over the residents'
privacy are progressing. Therefore, this paper presents SASSL, a secure aerial
surveillance drone using split learning to classify whether there is a presence
of a fire on the streets. This innovative split learning method transfers CCTV
footage captured with a drone to a nearby server to run a deep neural network
to detect a fire's presence in real-time without exposing the original data. We
devise a scenario where surveillance UAVs roam around the suburb, recording any
unnatural behavior. The UAV can process the recordings through its on-mobile
deep neural network system or transfer the information to a server. Due to the
resource limitations of mobile UAVs, the UAV does not have the capacity to run
an entire deep neural network on its own. This is where the split learning
method comes in handy. The UAV runs the deep neural network only up to the
first hidden layer and sends only the feature map to the cloud server, where
the rest of the deep neural network is processed. By ensuring that the learning
process is divided between the UAV and the server, the privacy of raw data is
secured while the UAV does not overexert its minimal resources.
