Lack of an accurate and low-cost method to reconstruct indoor maps is the main reason behind the current sporadic availability of digital building floor plans. The conventional approach using professional equipment is very costly and only available in the most popular areas. In this paper, we propose and demonstrate CrowdMap, a crowd sourcing system utilizing sensor-rich video data from mobile users for indoor floor plan reconstruction with low-cost. The key idea of CrowdMap is to first jointly leverage crowd sourced sensory and video data to track user movements, then use the inferred user motion traces and context of the image to produce an accurate floor plan. In particular, we exploit the sequential relationship between each consecutive frame abstracted from the video to improve system performance. Our experiments in three college buildings show that CrowdMap achieves a precision of hallway shape around 88%, a recall around 93% and a F-measure around 90%. In addition, we achieve on average 9.8% room area error and on average 6.5% room aspect ratio error. The evaluation result demonstrates a significant improvement of accuracy compared with other crowd sourcing floor plan reconstruction systems.

Crowdsourcing is a technology with the potential to revolutionize large-scale data gathering in an extremely cost-effective manner. It provides an unprecedented means of collecting data from the physical world, particularly through the use of modern smartphones, which are equipped with high-resolution cameras and various micro-electrical sensors. In this paper, we address the critical task of reconstructing the indoor interior view of a building from crowdsourced data. We propose, design, and prototype IndoorCrowd2D, a smartphone-empowered crowdsourcing system for indoor scene reconstruction. We first formulate the problem via trackable models and then employ a divide and conquer approach to address the inherently incomplete, opportunistic, and noisy crowdsourced data. By utilizing the image information and sensory data in a coordinated way, our system demonstrates high result-accuracy, as well as allows a gradual build-up procedure of the hallway skeleton. Our evaluation result shows that IndoorCrowd2D achieves a precision around 85%, a 100% recall and a F-score around 95% for reconstructing college buildings from 1,151 datasets uploaded by 25 users. This reveals that our image and sensor hybrid method is more robust to overcome errors and outliers as compared to image-only method.

The mobile users are facing a serious risk of losing location privacy (e.g., users' location information transmitted by open advertisement network, and the reported event of involuntary tracking of mobile users in popular mobile social apps). In this study, we design and implement LocMask, a system-level solution that provides location privacy protection in Android system. LocMask achieves the tradeoff of the privacy and the utility of location based services by providing the Quality of Protection (QoP) on demand, which sets different privacy protection levels to different locations based on how sensitive these locations are. Motivated by the fact that Top locations (e.g, user's home or office) are more sensitive than less visiting locations, LocMask provides location profile management module that records the user's mobility history and ranks the locations in terms of the user's visiting frequency. With users' location profiles, LocMask can automatically determines the sensitiveness of these locations as well as their corresponding privacy protection level. LocMask is also designed to incorporate various obfuscation techniques. The effectiveness of LocMask is supported by extensive real-world data based evaluations.

Location-based social networks (LBSNs) feature friend discovery by location proximity that has attracted hundreds of millions of users world-wide. While leading LBSN providers claim the well-protection of their users' location privacy, for the first time we show through real world attacks that these claims do not hold. In our identified attacks, a malicious individual with the capability of no more than a regular LBSN user can easily break most LBSNs by manipulating location information fed to LBSN client apps and running them as location oracles. We further develop an automated user location tracking system and test it on leading LBSNs including Wechat, Skout, and Momo. We demonstrate its effectiveness and efficiency via a 3 week real-world experiment on 30 volunteers and show that we could geo-locate any target with high accuracy and readily recover his/her top 5 locations. Finally, we also develop a framework that explores a grid reference system and location classifications to mitigate the attacks. Our result serves as a critical security reminder of the current LBSNs pertaining to a vast number of users.

Short-range wireless communication technologies have been used in many security-sensitive smartphone applications and services such as contactless micro payment and device pairing. Typically, the data confidentiality of the existing short-range communication systems relies on so-called “key-exchange then encryption” mechanism, which is inefficient, especially for short communication sessions. In this work, we present${\ssb{PriWhisper}}$—a keyless secure acoustic short-range communication system for smartphones. It is designed to provide a software-based solution to secure smartphone communication without the key agreement phase.${\ssb{PriWhisper}}$adopts the emerging friendly jamming technique from radio communication for data confidentiality. The system prototype is implemented and evaluated on several Android smartphone platforms for efficiency and usability. We theoretically and experimentally analyze the security of our proposed acoustic communication system against eavesdropping. In particular, we study the (in)separability of the data signal and jamming signal against blind signal segmentation (BSS) attacks such as independent component analysis (ICA). The result shows that${\ssb{PriWhisper}}$provides sufficient security guarantees for commercial smartphone applications and yet strong compatibilities with most legacy smartphone platforms. As an application, we also develop${\ssb{AcousAuth}}$—a novel smartphone-empowered system for personal authentication.

Cognitive Radio Network (CRN) is regarded as a promising way to address the increasing demand for wireless channel resources. It solves the channel resource shortage problem by allowing a Secondary User (SU) to access the channel of a Primary User (PU) when the channel is not occupied by the PU. The latest FCC's rule in May 2012 enforces database-driven CRNs, in which an SU queries a database to obtain spectrum availability information by submitting a location based query. However, one concern about database-driven CRNs is that the queries sent by SUs will inevitably leak the location information. In this study, we identify a new kind of attack against location privacy of database-drive CRNs. Instead of directly learning the SUs' locations from their queries, our discovered attacks can infer an SU's location through his used channels. We propose Spectrum Utilization based Location Inferring Algorithm that enables the attacker to geo-locate an SU. To thwart location privacy leaking from query process, we propose a novel Private Spectrum Availability Information Retrieval scheme that utilizes a blind factor to hide the location of the SU. To defend against the discovered attack, we propose a novel prediction based Private Channel Utilization protocol that reduces the possibilities of location privacy leaking by choosing the most stable channels. We implement our discovered attack and proposed scheme on the data extracted from Google Earth Coverage Maps released by FCC. Experiment results show that the proposed protocols can significantly improve the location privacy.

The Database-driven Cognitive Radio Network is regarded as a promising way for a better utilization of radio channels without introducing the interference to the primary user. However, it is also facing a series of security threats. In this study, we identify a new kind of location privacy related attack which could geo-locate a secondary user from the spectrum he used. We propose a Spectrum Utilization based Location Inference Algorithm, which is based on the intersection of the possible location sets revealed by each channel access or channel transition event under the presence of the primary user. We implement our algorithm on the data extracted from Google Earth Coverage Maps released by FCC. Our experiement results show that, $80\%$ SUs could be located to 10 cells based on 25 or less channels.

Mobile social networks are expected to substantially enrich interaction with ubiquitous computing environments by integrating social context information into local interactions. However, in mobile social networks, the mobile users may face the risk of leaking their personal information and their location privacy. In this study, we first model the secure friend discovery process as a generalized privacy-preserving interest/profile matching problem. Then, we identify a new security threat arising from existing secure friend discovery protocols, coined as runaway attack, which is expected to introduce serious fairness issue. To address this new threat, we introduce a novel blind vector transformation technique, which could hide the correlation between the original vector and the transformed result. Based on it, we propose our fairness-aware privacy preserving interest/profile matching protocol, which enables one party to match its interest with the profile of another, without revealing its real interest and profile and vice versa. The detailed security analysis as well as real-world implementations demonstrate the effectiveness and the efficiency of the proposed protocol.