@article{zhang_sabir_das_2023, title={Speaker Orientation-Aware Privacy Control to Thwart Misactivation of Voice Assistants}, ISSN={["1530-0889"]}, DOI={10.1109/DSN58367.2023.00061}, abstractNote={Smart home voice assistants (VAs) such as Amazon Echo and Google Home have become popular because of the convenience they provide through voice commands. VAs continuously listen to detect the wake command and send the subsequent audio data to the manufacturer-owned cloud service for processing to identify actionable commands. However, research has shown that VAs are prone to replay attack and accidental activations when the wake words are spoken in the background (either by a human or played through a mechanical speaker). Existing privacy controls are not effective in preventing such misactivations. This raises privacy and security concerns for the users as their conversations can be recorded and relayed to the cloud without their knowledge. Recent studies have shown that the visual gaze plays an important role when interacting with conservation agents such as VAs, and users tend to turn their heads or body toward the VA when invoking it. In this paper, we propose a device-free, non-obtrusive acoustic sensing system called HeadTalk to thwart the misactivation of VAs. The proposed system leverages the user's head direction information and verifies that a human generates the sound to minimize accidental activations. Our extensive evaluation shows that HeadTalk can accurately infer a speaker's head orientation with an average accuracy of 96.14% and distinguish human voice from a mechanical speaker with an equal error rate of 2.58%. We also conduct a user interaction study to assess how users perceive our proposed approach compared to existing privacy controls. Our results suggest that HeadTalk can not only enhance the security and privacy controls for VAs but do so in a usable way without requiring any additional hardware.}, journal={2023 53RD ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS, DSN}, author={Zhang, Shaohu and Sabir, Aafaq and Das, Anupam}, year={2023}, pages={597–610} }
 @article{zhang_li_das_2023, title={VoicePM: A Robust Privacy Measurement on Voice Anonymity}, DOI={10.1145/3558482.3590175}, abstractNote={Voice-based human-computer interaction has become pervasive in laptops, smartphones, home voice assistants, and Internet of Thing (IoT) devices. However, voice interaction comes with security and privacy risks. Numerous privacy-preserving measures have been proposed for hiding the speaker's identity while maintaining speech intelligibility. However, existing works do not consider the overall tradeoff between speech utility, speaker verification, and inference of voice attributes, including emotional state, age, accent, and gender. In this study, we first develop a tradeoff metric to capture voice biometrics as well as different voice attributes. We then propose VoicePM, a robust Voice Privacy Measurement framework, to study the feasibility of applying different state-of-the-art voice anonymization solutions to achieve the optimum tradeoff between privacy and utility. We conduct extensive experiments using anonymization approaches covering signal processing, voice synthesis, voice conversion, and adversarial techniques on three speech datasets that include both English and Chinese speakers to showcase the effectiveness and feasibility of VoicePM.}, journal={PROCEEDINGS OF THE 16TH ACM CONFERENCE ON SECURITY AND PRIVACY IN WIRELESS AND MOBILE NETWORKS, WISEC 2023}, author={Zhang, Shaohu and Li, Zhouyu and Das, Anupam}, year={2023}, pages={215–226} }
 @article{zhang_venkatnarayan_shahzad_2020, title={A WiFi-based Home Security System}, ISSN={["2155-6806"]}, DOI={10.1109/MASS50613.2020.00026}, abstractNote={Typical home security systems monitor homes for intrusions by installing contact sensors on doors and windows and motion sensors inside the house. Unfortunately, due to the high deployment and operational costs of today’s home security systems, only a small fraction of homes have security systems installed (e.g., only 17% in the US and 15% in China). In this paper, we propose a Wi Fi based H ome S ecurity system (WiHS) that uses commodity WiFi devices, which most modern households already have, to perform the three primary tasks of typical home security systems: 1) detect when a door/window is opened/closed, 2) identify which door/window has been opened/closed, and 3) detect movements inside the house. The design of WiHS is based on our intuitive and theoretical understanding of the impacts of the movements of doors and windows on WiFi signals, which we will develop and present in this paper. We extensively evaluated WiHS using commodity WiFi devices in 3 different houses. WiHS detected intrusions with over 95% accuracy and identified the exact door/window that moved with just 4.5% average error.}, journal={2020 IEEE 17TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SMART SYSTEMS (MASS 2020)}, author={Zhang, Shaohu and Venkatnarayan, Raghav H. and Shahzad, Muhammad}, year={2020}, pages={129–137} }
 @article{wood_zhang_2021, title={Evaluating Relationships between Perception-Reaction Times, Emergency Deceleration Rates, and Crash Outcomes using Naturalistic Driving Data}, volume={2675}, ISSN={["2169-4052"]}, DOI={10.1177/0361198120966602}, abstractNote={Perception-reaction time (PRT) and deceleration rate are two key components in geometric design of highways and streets. Combined with a design speed, they determine the minimum required stopping sight distance (SSD). Current American Association of Highway Transportation Officials (AASHTO) SSD guidance is based on 90th percentile PRT and 10th percentile deceleration rate values from experiments completed in the mid-1990s. These experiments lacked real-world distractions, and so forth. Thus, the values from these experiments may not be applicable in real-world scenarios. This research evaluated (1) differences in PRTs and deceleration rates between crash and near-crash events and (2) developed predictive models for PRT and deceleration rate that could be used for roadway design. This was accomplished using (1) genetic matching (with Rosenbaum’s sensitivity analysis) and (2) quantile regression. These methods were applied to the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) data.The analysis results indicated that there were differences in PRT and deceleration rates for crash and near-crash events. The specific estimates were that, on average, drivers involved in crash events took 0.487 s longer to react and decelerated at 0.018 g’s (0.58 ft/s2) slower than drivers in equivalent near-crashes. Prediction models were developed for use in roadway design. These models were used to develop tables comparing existing SSD design criteria with SSD criteria based on the results of the predictive models. These predicted values indicated that minimum design SSD values would increase by 10.5–129.2 ft, dependent on the design speed and SSD model used.}, number={1}, journal={TRANSPORTATION RESEARCH RECORD}, author={Wood, Jonathan S. and Zhang, Shaohu}, year={2021}, month={Jan}, pages={213–223} }
 @article{wood_zhang_2018, title={Identification and Calculation of Horizontal Curves for Low-Volume Roadways Using Smartphone Sensors}, volume={2672}, ISSN={["2169-4052"]}, DOI={10.1177/0361198118759005}, abstractNote={ Horizontal curves are a contributing factor to the number of observed roadway crashes. Identifying locations and geometric characteristics of horizontal curves plays a crucial role in crash prediction and prevention. However, most states in the USA face a challenge in maintaining detailed and high-quality roadway inventory databases for low-volume rural roads due to the labor-intensive and time-consuming nature of collecting and maintaining the data. This paper proposes a low-cost mobile road inventory system for two-lane horizontal curves based on off-the-shelf smartphones. The proposed system is capable of accurately detecting horizontal curves by exploiting a K-means machine learning technique. Butterworth low-pass filtering is applied to reduce sensor noise. Extended Kalman filtering is adopted to improve the GPS accuracy. Chord method-based radius computation and superelevation estimation are introduced to achieve accurate and robust results despite the low-frequency GPS and noisy sensor signals obtained from smartphones. This study implements this method using an Android-based smartphone and tests 21 horizontal curves in South Dakota. The results demonstrate that the proposed system achieves high curve identification accuracy as well as high accuracy for calculating curve radius and superelevation. }, number={39}, journal={TRANSPORTATION RESEARCH RECORD}, author={Wood, Jonathan S. and Zhang, Shaohu}, year={2018}, month={Dec}, pages={1–10} }