Indoor navigation systems are very useful in large complex indoor environments such as shopping malls. Current systems focus on improving indoor localization accuracy and must be combined with an accurate labeled floor plan to provide usable indoor navigation services. Such labeled floor plans are often unavailable or involve a prohibitive cost to manually obtain. In this paper, we present IndoorWaze, a novel crowdsourcing-based context-aware indoor navigation system that can automatically generate an accurate context-aware floor plan with labeled indoor POIs for the first time in literature. IndoorWaze combines the Wi-Fi fingerprints of indoor walkers with the Wi-Fi fingerprints and POI labels provided by POI employees to produce a high-fidelity labeled floor plan. As a lightweight crowdsourcing-based system, IndoorWaze involves very little effort from indoor walkers and POI employees. We prototype IndoorWaze on Android smartphones and evaluate it in a large shopping mall. Our results show that IndoorWaze can generate a high-fidelity labeled floor plan, in which all the stores are correctly labeled and arranged, all the pathways and crossings are correctly shown, and the median estimation error for the store dimension is below 12%.

Indoor positioning systems (IPSes) can enable many location-based services in large indoor venues where GPS signals are unavailable or unreliable. Among the most viable types of IPSes, RSS-IPSes rely on ubiquitous smartphones and indoor WiFi infrastructures and explore distinguishable received signal strength (RSS) measurements at different indoor locations as their location fingerprints. RSS-IPSes are unfortunately vulnerable to physical-layer RSS attacks that cannot be thwarted by conventional cryptographic techniques. Existing defenses against RSS attacks are all subject to an inherent tradeoff between indoor positioning accuracy and attack resilience. This paper presents the design and evaluation of MV-IPS, a novel RSS-IPS based on weighted multi-voting, which does not suffer from this tradeoff. In MV-IPS, every WiFi access point (AP) that receives a user's RSS measurement gives a weighted vote for every reference location, and the reference location that receives the highest accumulative votes from all APs is output as the user's most likely position. Trace-driven simulation studies based on real RSS measurements demonstrate that MV-IPS can achieve much higher positioning accuracy than prior solutions no matter whether RSS attacks are present.

Indoor positioning systems (IPSes) can enable many location-based services in large indoor environments where GPS is not available or reliable. Mobile crowdsourcing is widely advocated as an effective way to construct IPS maps. This paper presents the first systematic study of security issues in crowd-sourced WiFi-based IPSes to promote security considerations in designing and deploying crowdsourced IPSes. We identify three attacks on crowdsourced WiFi-based IPSes and propose the corresponding countermeasures. The efficacy of the attacks and also our countermeasures are experimentally validated on a prototype system. The attacks and countermeasures can be easily extended to other crowdsourced IPSes.

Mobile two-factor authentication (2FA) has become commonplace along with the popularity of mobile devices. Current mobile 2FA solutions all require some form of user effort which may seriously affect the experience of mobile users, especially senior citizens or those with disability such as visually impaired users. In this paper, we propose Proximity-Proof, a secure and usable mobile 2FA system without involving user interactions. Proximity-Proof automatically transmits a user's 2FA response via inaudible OFDM-modulated acoustic signals to the login browser. We propose a novel technique to extract individual speaker and microphone fingerprints of a mobile device to defend against the powerful man-in-the-middle (MiM) attack. In addition, Proximity-Proof explores two-way acoustic ranging to thwart the co-located attack. To the best of our knowledge, Proximity-Proof is the first mobile 2FA scheme resilient to the MiM and co-located attacks. We empirically analyze that Proximity-Proof is at least as secure as existing mobile 2FA solutions while being highly usable. We also prototype Proximity-Proof and confirm its high security, usability, and efficiency through comprehensive user experiments.

Keystroke inference attacks pose an increasing threat to ubiquitous mobile devices. This paper presents EyeTell, a novel video-assisted attack that can infer a victim's keystrokes on his touchscreen device from a video capturing his eye movements. EyeTell explores the observation that human eyes naturally focus on and follow the keys they type, so a typing sequence on a soft keyboard results in a unique gaze trace of continuous eye movements. In contrast to prior work, EyeTell requires neither the attacker to visually observe the victim's inputting process nor the victim device to be placed on a static holder. Comprehensive experiments on iOS and Android devices confirm the high efficacy of EyeTell for inferring PINs, lock patterns, and English words under various environmental conditions.

In our information-driven web-based society, we are all gradually falling ""victims"" to information overload [5].However, while sighted people are finding ways to sift through information faster, Internet users who are blind are experiencing an even greater information overload. These people access computers and Internet using screen-reader software, which reads the information on a computer screen sequentially using computer-generated speech. While sighted people can learn how to quickly glance over the headlines and news articles online to get the gist of information, people who are blind have to use keyboard shortcuts to listen through the content narrated by a serial audio interface. This interface does not give them an opportunity to know what content to skip and what to listen to. So, they either listen to all of the content or listen to the first part of each sentence or paragraph before they skip to the next one. In this paper, we propose an automated approach to facilitate non-visual skimming of web pages. We describe the underlying algorithm, outline a non-visual skimming interface, and report on the results of automated experiments, as well as on our user study with 23 screen-reader users. The results of the experiments suggest that we have been moderately successful in designing a viable algorithm for automatic summarization that could be used for non-visual skimming. In our user studies, we confirmed that people who are blind could read and search through online articles faster and were able to understand and remember most of what they have read with our skimming system. Finally, all 23 participants expressed genuine interest in using non-visual skimming in the future.

In this paper, we propose an automatic approach, complete with a prototype system, for supporting instant access to maps for local navigation by people with visual impairment. The approach first detects and segments texts from a map image and recreates the remaining graphical parts in a tactile form which can be reproduced immediately through a tactile printer. Then, it generates an SVG (Scalable Vector Graphics) file, which integrates both text and graphical information. The tactile hardcopy and the SVG file together are used to provide a user with interactive access to the map image through a touchpad, resulting in a tactile-audio representation of the original input image. This supports real-time access to the map without tedious conversion by a sighted professional. Evaluations with six users who are blind show that the created tactile-audio maps from our prototype system convey the most important map information and are deemed as potentially useful for local navigation.

While navigation within complex information spaces is a problem for all users, the problem is most evident with individuals who are blind who cannot simply locate, point, and click on a link in hypertext documents with a mouse. Users who are blind have to listen searching for the link in the document using only the keyboard and a screen reader program, which may be particularly inefficient in large documents with many links or deep hierarchies that are hard to navigate. Consequently, they are especially penalized when the information being searched is hidden under multiple layers of indirections. In this article, we introduce a segment-enrich-annotate (SEA) paradigm for adapting digital content with deep structures for improved accessibility. In particular, we instantiate and evaluate this paradigm through the iCare-Assistant, an assistive system for helping students who are blind in accessing Web and electronic course materials. Our evaluations, involving the participation of students who are blind, showed that the iCare-Assistant system, built based on the SEA paradigm, reduces the navigational overhead significantly and enables user who are blind access complex online course servers effectively.

This demonstration will show the features and function of the portable iCARE Reader device, which allows people who are blind or visually impaired to read books (and other forms of printed text) in a more natural and convenient way than current tabletop flatbed scanner systems.