We propose a novel modality for active biometric authentication: electrical muscle stimulation (EMS). To explore this, we engineered an interactive system, which we call ElectricAuth, that stimulates the user’s forearm muscles with a sequence of electrical impulses (i.e., EMS challenge) and measures the user’s involuntary finger movements (i.e., response to the challenge). ElectricAuth leverages EMS’s intersubject variability, where the same electrical stimulation results in different movements in different users because everybody’s physiology is unique (e.g., differences in bone and muscular structure, skin resistance and composition, etc.). As such, ElectricAuth allows users to login without memorizing passwords or PINs.

Digital screens, such as liquid crystal displays (LCDs), are vulnerable to attacks (e.g., "shoulder surfing") that can bypass security protection services (e.g., firewall) to steal confidential information from intended victims. The conventional practice to mitigate these threats is isolation. An isolated zone, without accessibility, proximity, and line-of-sight, seems to bring personal devices to a truly secure place.In this paper, we revisit this historical topic and re-examine the security risk of screen attacks in an isolation scenario mentioned above. Specifically, we identify and validate a new and practical side-channel attack for screen content via liquid crystal nematic state estimation using a low-cost radio-frequency sensor. By leveraging the relationship between the screen content and the states of liquid crystal arrays in displays, we develop WaveSpy, an end-to-end portable through-wall screen attack system. WaveSpy comprises a low-cost, energy-efficient and light-weight millimeter-wave (mmWave) probe which can remotely collect the liquid crystal state response to a set of mmWave stimuli and facilitate screen content inference, even when the victim’s screen is placed in an isolated zone. We intensively evaluate the performance and practicality of WaveSpy in screen attacks, including over 100 different types of content on 30 digital screens of modern electronic devices. WaveSpy achieves an accuracy of 99% in screen content type recognition and a success rate of 87.77% in Top-3 sensitive information retrieval under real-world scenarios, respectively. Furthermore, we discuss several potential defense mechanisms to mitigate screen eavesdropping similar to WaveSpy.

Digital devices have become a necessity in our daily life, with digital screens acting as a gateway to access a plethora of information present in the underlying device. However, these devices emit visible light through screens where long-term use can lead to significant screen exposure, further influencing users' health. Conventional methods on screen exposure detection (\textite.g., photo logger) are usually privacy-invasive and expensive, further, require ideal light conditions, which are unattainable in real practice. Considering the light intensity and spectrum vary among different light sources, an effective screen spectrum estimation can provide vital information about screen exposure. To this end, we first investigate the characteristics of the junction between p-type and n-type semiconductor (i.e., PN junction) to sense the spectrum under various conditions. Empirically, we design and implement, \textsfSpecEye, an end-to-end, low cost, wearable, and privacy-preserving screen exposure detection system with a mobile application. For validating the performance of our system, we conduct comprehensive experiments with $54$ commodity digital screens, at $43$ distinct locations, with results showing a base accuracy of $99$%, and an equal error rate (EER) approaching $0.80$% under the controlled lab setup. Moreover, we assess the reliability, robustness, and performance variation of \textsfSpecEye under various real-world circumstances to observe a stable accuracy of $95$%. Our real-world study indicates \textsfSpecEye is a promising system for screen exposure detection in everyday life.

Hidden electronics possess the risk of both security threat and privacy intrusion. We present a wireless hidden electronic recognition system, through electronic components unique mmWave nonlinear responses to identify the threats. We then evaluate E-Eye's performance and robustness with a controlled experiment and a field study using iconic devices and score the system with metrics. Results prove that E-Eye is an accurate and robust hidden electronic recognition system.

Inventory management is pivotal in the supply chain to supervise the non-capitalized products and stock items. Item counting, indexing and identification are the major jobs of inventory management. Currently, the most adopted inventory technologies in product counting/identification are using either the laser-scannable barcode or the radio-frequency identification (RFID). However, the laser-scannable barcode is entangled by an alignment issue (i.e., the laser reader must align with one barcode in line-of-sight), and the RFID is economically and environmentally unfriendly (i.e., high-cost and not naturally disposable). To this end, we propose FerroTag which is a paper-based mmWave-scannable tagging infrastructure for the next generation inventory management system, featuring ultra-low cost, environment-friendly, battery-free and in-situ (i.e., multiple tags can be simultaneously processed outside the line-of-sight). FerroTag is developed on top of the FerroRF effects. Specifically, the magnetic nanoparticles within the ferrofluidic ink reply to probing mmWave with classifiable features (i.e., the FerroRF response). By designating the ink pattern and hence the location of particles, the related FerroRF response can be modified. Thus, a specifically designated ferrofluidic ink printed pattern, which is associated with a unique FerroRF response, is a remotely retrievable (a.k.a., mmWave-scannable) identity. Furthermore, we augment FerroTag by designing a high capacity pattern system and a fine-grained identification protocol such that the capacity and robustness of FerroTag can be systematically improved in mass product management in inventory. Last but not least, we evaluate the performance of FerroTag with 201 different tag design patterns. Results show that FerroTag can identify tags with an accuracy of more than 99% in a controlled lab setup. Moreover, we examine the reliability, robustness and performance of FerroTag under various real-world circumstances, where FerroTag maintains the accuracy over 97%. Therefore, FerroTag is a promising tagging infrastructure for the applications in inventory management systems.

With the growth of the elder population, fall risk evaluation is crucial to prevent elders from serious injuries, as well as reduce related financial burdens. A balance assessment, timed up and go (TUG), has been widely used to estimate fall risk. The standardized TUG focuses on flat ground walking with no environmental variance. Therefore, it falls short of assessing an individual's gait adaptability. Being able to adjust steps in response to environmental changes, for example, needing to navigate around or over a child's toy left on the sidewalk, is essential to avoid fall risk and fundamental to community ambulation. To this end, we propose four environment-adapting TUGs designed to assess one's ability to adapt gait in complex environments and a compatible system named Smart Insole TUG (SITUG), which provides real-time, feature-rich, and ease-of-operation TUG analysis. Based on experimental results, SITUG is capable of extracting gait related spatial-temporal features with all mean accuracies over 92%. Besides, the system achieves a mean accuracy of 92.23% in segmenting five TUG phases.

No abstract available.

While malicious attacks on electronic devices (e-devices) have become commonplace, the use of e-devices themselves for malicious attacks has increased (e.g., explosives and eavesdropping). Modern e-devices (e.g., spy cameras, bugs or concealed weapons) can be sealed in parcels/boxes, hidden under clothing or disguised with cardboard to conceal their identities (named as hidden e-devices hereafter), which brings challenges in security screening. Inspection equipment (e.g., X-ray machines) is bulky and expensive. Moreover, screening reliability still rests on human performance, and the throughput in security screening of passengers and luggages is very limited. To this end, we propose to develop a low-cost and practical hidden e-device recognition technique to enable efficient screenings for threats of hidden electronic devices in daily life. First, we investigate and model the characteristics of nonlinear effects, a special passive response of electronic devices under millimeter-wave (mmWave) sensing. Based on this theory and our preliminary experiments, we design and implement, E-Eye, an end-to-end portable hidden electronics recognition system. E-Eye comprises a low-cost (i.e., under $100), portable (i.e., 11.8cm by 4.5cm by 1.8cm) and light-weight (i.e., 45.5g) 24GHz mmWave probe and a smartphone-based e-device recognizer. To validate the E-Eye performance, we conduct experiments with 46 commodity electronic devices under 39 distinct categories. Results show that E-Eye can recognize hidden electronic devices in parcels/boxes with an accuracy of more than 99% and has an equal error rate (EER) approaching 0.44% under a controlled lab setup. Moreover, we evaluate the reliability, robustness and performance variation of E-Eye under various real-world circumstances, and E-Eye can still achieve accuracy over 97%. Intensive evaluation indicates that E-Eye is a promising solution for hidden electronics recognition in daily life.