MobiSys 2016

The 14th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2016) spanned a range of themes and domains, from smart environments to security and privacy. The highlights presented here cover the keynotes, paper sessions, and first Asian Students Symposium on Emerging Technologies

Survey and Systematization of Secure Device Pairing

Secure Device Pairing (SDP) schemes have been developed to facilitate secure communications among smart devices, both personal mobile devices and Internet of Things (IoT) devices. Comparison and assessment of SDP schemes is troublesome, because each scheme makes different assumptions about out-of-band channels and adversary models, and are driven by their particular use-cases. A conceptual model that facilitates meaningful comparison among SDP schemes is missing. We provide such a model. In this article, we survey and analyze a wide range of SDP schemes that are described in the literature, including a number that have been adopted as standards. A system model and consistent terminology for SDP schemes are built on the foundation of this survey, which are then used to classify existing SDP schemes into a taxonomy that, for the first time, enables their meaningful comparison and analysis.The existing SDP schemes are analyzed using this model, revealing common systemic security weaknesses among the surveyed SDP schemes that should become priority areas for future SDP research, such as improving the integration of privacy requirements into the design of SDP schemes. Our results allow SDP scheme designers to create schemes that are more easily comparable with one another, and to assist the prevention of persisting the weaknesses common to the current generation of SDP schemes.Comment: 34 pages, 5 figures, 3 tables, accepted at IEEE Communications Surveys & Tutorials 2017 (Volume: PP, Issue: 99

Radio2Text: Streaming Speech Recognition Using mmWave Radio Signals

Millimeter wave (mmWave) based speech recognition provides more possibility for audio-related applications, such as conference speech transcription and eavesdropping. However, considering the practicality in real scenarios, latency and recognizable vocabulary size are two critical factors that cannot be overlooked. In this paper, we propose Radio2Text, the first mmWave-based system for streaming automatic speech recognition (ASR) with a vocabulary size exceeding 13,000 words. Radio2Text is based on a tailored streaming Transformer that is capable of effectively learning representations of speech-related features, paving the way for streaming ASR with a large vocabulary. To alleviate the deficiency of streaming networks unable to access entire future inputs, we propose the Guidance Initialization that facilitates the transfer of feature knowledge related to the global context from the non-streaming Transformer to the tailored streaming Transformer through weight inheritance. Further, we propose a cross-modal structure based on knowledge distillation (KD), named cross-modal KD, to mitigate the negative effect of low quality mmWave signals on recognition performance. In the cross-modal KD, the audio streaming Transformer provides feature and response guidance that inherit fruitful and accurate speech information to supervise the training of the tailored radio streaming Transformer. The experimental results show that our Radio2Text can achieve a character error rate of 5.7% and a word error rate of 9.4% for the recognition of a vocabulary consisting of over 13,000 words.Comment: Accepted by Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (ACM IMWUT/UbiComp 2023

Inaudible acoustics: Techniques and applications

This dissertation is focused on developing a sub-area of acoustics that we call inaudible acoustics. We have developed two core capabilities, (1) BackDoor and (2) Ripple, and demonstrated their use in various mobile and IoT applications. In BackDoor, we synthesize ultrasound signals that are inaudible to humans yet naturally recordable by all microphones. Importantly, the microphone does not require any modification, enabling billions of microphone-enabled devices, including phones, laptops, voice assistants, and IoT devices, to leverage the capability. Example applications include acoustic data beacons, acoustic watermarking, and spy-microphone jamming. In Ripple, we develop modulation and sensing techniques for vibratory signals that traverse through solid surfaces, enabling a new form of secure proximal communication. Applications of the vibratory communication system include on-body communication through imperceptible physical vibrations and device-device secure data transfer through physical contacts. Our prototypes include an inaudible jammer that secures private conversations from electronic eavesdropping, acoustic beacons for location-based information sharing, and vibratory communication in a smart-ring sending password through a finger touch. Our research also uncovers new security threats to acoustic devices. While simple abuse of inaudible jammer can disable hearing aids and cell phones, our work shows that voice interfaces, such as Amazon Echo, Google Home, Siri, etc., can be compromised through carefully designed inaudible voice commands. The contributions of this dissertation can be summarized in three primitives: (1) exploiting inherent hardware nonlinearity for sensing out-of-band signals, (2) developing the vibratory communication system for secure touch-based data exchange, and (3) structured information reconstruction from noisy acoustic signals. In developing these primitives, we draw from principles in wireless networking, digital communications, signal processing, and embedded design and translate them to completely functional systems