# Secure Audio-Visual Data Exchange for Android In-Vehicle Ecosystems
## 1 Introduction and Related Work
### Context and Related Work  
First, in order to clarify the context, we provide a brief summary of commonly employed wireless communication channels in Table 1, i.e., Wi-Fi, Bluetooth and the audio channel which is within the scope of this work. The table outlines various characteristics related to bandwidth, range, operating frequency, security and the area of application. Notably, audio channels have been recently explored by various papers which address device  
Figure 1: The addressed setting: key exchange between a phone and a head unit (or another phone). pairing as they have a security advantage since acoustic signals are not so easy to intercept by outsiders. Of course, in terms of bandwidth and range, sounds are much less efficient than Wi-Fi or Bluetooth. However, this can also be interpreted as a security advantage since sound has a much shorter range and thus it will be harder to eavesdrop. For the same security reason, channels with an even shorter range such as NFC are sometimes preferred. However, commercial in-vehicle head units do not always possess NFC capabilities and such a solution will have a reduced practical coverage for the in-vehicle ecosystem. The authors in [4] use audio signals to exchange a Wi-Fi network's SSID and password. Each character from the exchanged data is encoded by one frequency using the range between 2 kHz and 5 kHz. For the same purpose, in [10] the Wi-Fi password is shared between friends using a social network graph. Also, in [11] a server is used as a third-party instance to share the Wi-Fi password. In [12] a device pairing system is proposed for smartwatches which uses audio signals. The authors use a short-time Fourier transform and the resulted signal energy to compute a matrix used for key generation. A fuzzy cryptographic scheme for communication between devices based on audio fingerprints extracted from the signal energy and discrete Fourier transform of the ambient audio signal is presented in [13]. In [14] the audio channel is used to exchange data between wireless devices. Also, in [15] isolated sounds and ultrasonic sounds are used as covert channels for mobile device communication. An authentication mechanism based on ambient sound is proposed in [16]. The one-third octave band filtering and the cross-correlation of the recorded sound are used to detect the sound similarity. An audio-based authentication system between a server and two devices is proposed in [17]. The audio signal contains symbols which correspond to a certain frequency between 16 kHz and 19 kHz. A system for device authentication based on the Fast Fourier Transform (FFT) of the audio signal is proposed in [18]. In [19], a key extraction mechanism based on inaudible sounds is discussed. A Wi-Fi connection using audio is depicted in [20]. Also, in [21; 22], a smart device pairing system based on ambient noise is proposed. Not distant from these, there are several lines of work that employed environmental data for secure device pairing. A procedure for device pairing based on ambient sound and on a generated sine wave is presented in [23]. An approach for device pairing based on environmental data extracted from the smartphone accelerometers in distinct transportation modes is proposed in [24]. Solutions for pairing devices based on accelerometer data are proposed in [25; 26]. A system for wireless pairing using accelerometer data and heuristic trees is discussed in [27]. Shaking devices for pairing them is proposed in [28; 29].