Drones have created a new threat to people's privacy. We are now in an era in which anyone with a drone equipped with a video camera can use it to invade a subject's privacy by streaming the subject in his her private space over an encrypted first person view (FPV) channel. Although many methods have been suggested to detect nearby drones, they all suffer from the same shortcoming: they cannot identify exactly what is being captured, and therefore they fail to distinguish between the legitimate use of a drone (for example, to use a drone to film a selfie from the air) and illegitimate use that invades someone's privacy (when the same operator uses the drone to stream the view into the window of his neighbor's apartment), a distinction that in some cases depends on the orientation of the drone's video camera rather than on the drone's location. In this paper we shatter the commonly held belief that the use of encryption to secure an FPV channel prevents an interceptor from extracting the POI that is being streamed. We show methods that leverage physical stimuli to detect whether the drone's camera is directed towards a target in real time. We investigate the influence of changing pixels on the FPV channel (in a lab setup). Based on our observations we demonstrate how an interceptor can perform a side-channel attack to detect whether a target is being streamed by analyzing the encrypted FPV channel that is transmitted from a real drone (DJI Mavic) in two use cases: when the target is a private house and when the target is a subject.
We analyze three new consumer electronic gadgets in order to gauge the privacy and security trends in mass-market UbiComp devices. Our study of the Slingbox Pro uncovers a new information leakage vector for encrypted streaming multimedia. By exploiting properties of variable bitrate encoding schemes, we show that a passive adversary can determine with high probability the movie that a user is watching via her Slingbox, even when the Slingbox uses encryption. We experimentally evaluated our method against a database of over 100 hours of network traces for 26 distinct movies. Despite an opportunity to provide significantly more location privacy than existing devices, like RFIDs, we find that an attacker can trivially exploit the Nike+iPod Sport Kit's design to track users; we demonstrate this with a GoogleMaps-based distributed surveillance system. We also uncover security issues with the way Microsoft Zunes manage their social relationships. We show how these products' designers could have significantly raised the bar against some of our attacks. We also use some of our attacks to motivate fundamental security and privacy challenges for future UbiComp devices.