Voice interaction, as an emerging human-computer interaction method, has gained great popularity, especially on smart devices. However, due to the open nature of voice signals, voice interaction may cause privacy leakage. In this paper, we propose a novel scheme, calledSeVI, to protect voice interaction from being deliberately or unintentionally eavesdropped. SeVI actively generates jamming noise of superior characteristics, while a user is performing voice interaction with his/her device, so that attackers cannot obtain the voice contents of the user. Meanwhile, the device leverages the prior knowledge of the generated noise to adaptively cancel received noise, even when the device usage environment is changing due to movement, so that the user voice interactions are unaffected. SeVI relies on only normal microphone and speakers and can be implemented as light-weight software. We have implemented SeVI on a commercial off-the-shelf (COTS) smartphone and conducted extensive real-world experiments. The results demonstrate that SeVI can defend both online eavesdropping attacks and offline digital signal processing (DSP) analysis attacks.

Acquiring the geographical distribution of neighbors can support more adaptive media access control (MAC) protocols and other safety applications in Vehicular ad hoc network (VANETs). However, it is very challenging for each vehicle to estimate its own neighbor distribution in a fully distributed setting. In this paper, we propose an online distributed neighbor distribution estimation scheme, called PeerProbe, in which vehicles collaborate with each other to probe their own neighborhood via simultaneous symbol-level wireless communication. An adaptive compressive sensing algorithm is developed to recover a neighbor distribution based on a small number of random probes with non-negligible noise. Moreover, the needed number of probes adapts to the sparseness of the distribution. We implement a prototype system to verify the feasibility of PeerProbe in various typical vehicular channel conditions. We further conduct extensive simulations and the results demonstrate that PeerProbe is lightweight and can accurately recover highly dynamic neighbor distributions in critical channel conditions.

Finding a small object (e.g., earbuds, keys or a wallet) in an indoor environment (e.g., in a house or an office) can be frustrating. In this paper, we propose an innovative system, calledHyperEar, to localize such an object using only a single smartphone, based on enhanced time-difference-of-arrival (TDoA) measurements over acoustic signals issued from the object. One major challenge is the hardware limitations of a Commercial-Off-The-Shelf (COTS) phone with a short separation between the two microphones and the low sampling rate of such microphones. HyperEar enhances the accuracy of TDoA measurements by virtually increasing distances between microphones through sliding the phone in the air. HyperEar requires no communication for synchronization between the phone and the object and is a low-cost and easy-to-use system. We evaluate the performance of HyperEar via extensive experiments in various indoor conditions and the results demonstrate that, for an object of 7 m away, HyperEar can achieve a mean localization accuracy of about 15 cm when the object in normal indoor environments.

Voice interaction, as an emerging human-computer interaction method, has gained great popularity, especially on smart devices. However, due to the open nature of voice signals, voice interaction may cause privacy leakage. In this paper, we propose a novel scheme, called SeVI, to protect voice interaction from being deliberately or unintentionally eavesdropped. SeVI actively generates jamming noise of superior characteristics, while a user is performing voice interaction with his/her device, so that attackers cannot obtain the voice contents of the user. Mean-while, the device leverages the prior knowledge of the generated noise to adaptively cancel received noise, even when the device usage environment is changing due to movement, so that the user voice interactions are unaffected. SeVI relies on only normal microphone and speakers and can be implemented as light-weight software. We have implemented SeVI on a commercial off-the- shelf (COTS) smartphone and conducted extensive real-world experiments. The results demonstrate that SeVI can defend both online eavesdropping attacks and offline digital signal processing (DSP) analysis attacks.