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51
Taplogger: Inferring user inputs on smartphone touchscreens using on-board motion sensors
- In Proceedings of the fifth ACM conference on Wireless network security
, 2012
"... Today’s smartphones are shipped with various embedded motion sensors, such as the accelerometer, gyroscope, and orientation sensors. These motion sensors are useful in supporting the mobile UI innovation and motion-based commands. However, they also bring potential risks of leaking user’s private in ..."
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Cited by 34 (2 self)
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Today’s smartphones are shipped with various embedded motion sensors, such as the accelerometer, gyroscope, and orientation sensors. These motion sensors are useful in supporting the mobile UI innovation and motion-based commands. However, they also bring potential risks of leaking user’s private information as they allow third party applications to monitor the motion changes of smartphones. In this paper, we study the feasibility of inferring a user’s tap inputs to a smartphone with its integrated motion sensors. Specifically, we utilize an installed trojan application to stealthily monitor the movement and gesture changes of asmartphoneusingitson-boardmotionsensors.Whenthe user is interacting with the trojan application, it learns the motion change patterns of tap events. Later, when the user is performing sensitive inputs, such as entering passwords on the touchscreen, the trojan application applies the learnt pattern to infer the occurrence of tap events on the touchscreen as well as the tapped positions on the touchscreen. For demonstration, we present the design and implementation of TapLogger, a trojan application for the Android platform, which stealthily logs the password of screen lock and the numbers entered during a phone call (e.g., credit card and PIN numbers). Statistical results are presented to show the feasibility of such inferences and attacks.
Deanonymizing mobility traces: Using social network as a side-channel
- in Proceedings of the ACM Conference on Computer and Communications Security (CCS
, 2012
"... Location-based services, which employ data from smartphones, vehicles, etc., are growing in popularity. To reduce the threat that shared location data poses to a user’s privacy, some services anonymize or obfuscate this data. In this paper, we show these methods can be effectively defeated: a set of ..."
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Cited by 31 (2 self)
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Location-based services, which employ data from smartphones, vehicles, etc., are growing in popularity. To reduce the threat that shared location data poses to a user’s privacy, some services anonymize or obfuscate this data. In this paper, we show these methods can be effectively defeated: a set of location traces can be deanonymized given an easily obtained social network graph. The key idea of our approach is that a user may be identified by those she meets: a contact graph identifying meetings between anonymized users in a set of traces can be structurally correlated with a social network graph, thereby identifying anonymized users. We demonstrate the effectiveness of our approach using three real world datasets: University of St Andrews mobility trace and social network (27 nodes each), SmallBlue contact trace and Facebook social network (125 nodes), and Infocom 2006 bluetooth contact traces and conference attendees ’ DBLP social network (78 nodes). Our experiments show that 80 % of users are identified precisely, while only 8 % are identified incorrectly, with the remainder mapped to a small set of users.
How Long to Wait?: Predicting Bus Arrival Time with Mobile Phone based Participatory Sensing
"... The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers ’ participatory sens ..."
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Cited by 28 (3 self)
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The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers ’ participatory sensing. With commodity mobile phones, the bus passengers ’ surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resort to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.
Tracking unmodified smartphones using wi-fi monitors
- In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, SenSys ’12
, 2012
"... Smartphones with Wi-Fi enabled periodically transmit Wi-Fi messages, even when not associated to a network. In one 12-hour trial on a busy road (average daily traffic count 37,000 according to the state DOT), 7,000 unique devices were detected by a single road-side monitoring station, or about 1 dev ..."
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Cited by 22 (0 self)
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Smartphones with Wi-Fi enabled periodically transmit Wi-Fi messages, even when not associated to a network. In one 12-hour trial on a busy road (average daily traffic count 37,000 according to the state DOT), 7,000 unique devices were detected by a single road-side monitoring station, or about 1 device for every 5 vehicles. In this paper, we describe a system for passively tracking unmodified smartphones, based on such Wi-Fi detections. This system uses only common, off-the-shelf access point hardware to both collect and deliver detections. Thus, in addition to high detection rates, it potentially offers very low equipment and installation cost. However, the long range and sparse nature of our opportunistically collected Wi-Fi transmissions presents a significant localization challenge. We propose a trajectory estimation method based on Viterbi’s algorithm which takes second-by-second detections of a moving device as input, and produces the most likely spatio-temporal path taken. In addition, we present several methods that prompt passing devices to send additional messages, increasing detection rates an use signal-strength for improved accuracy. Based on our experimental evaluation from one 9-month deployment and several single-day deployments, passive Wi-Fi tracking detects a large fraction of passing smartphones, and produces high-accuracy trajectory estimates. 1
IODetector: A generic service for indoor outdoor detection
- In SenSys’ 12
"... The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper layer mobile applications. In this paper, we present IODetector: a lightweight sensing service which runs on the mobile phone and detects the indoor/outdoor environment ..."
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Cited by 19 (5 self)
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The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper layer mobile applications. In this paper, we present IODetector: a lightweight sensing service which runs on the mobile phone and detects the indoor/outdoor environment in a fast, accurate, and efficient manner. Constrained by the energy budget, IODetector leverages primarily lightweight sensing resources including light sensors, magnetism sensors, celltower signals, etc. For universal applicability, IODetector assumes no prior knowledge (e.g., fingerprints) of the environment and uses only on-board sensors common to mainstream mobile phones. Being a generic and lightweight service component, IODetector greatly benefits many location-based and context-aware applications. We prototype the IODetector on Android mobile phones and evaluate the system comprehensively with data collected from 19 traces which include 84 different places during one month period, employing different phone models. We further perform a case study where we make use of IODetector to instantly infer the GPS availability and localization accuracy in different indoor/outdoor environments.
Sensing Vehicle Dynamics for Determining Driver Phone Use
"... This paper utilizes smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences in centripetal acceleration due to vehicle dyn ..."
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Cited by 14 (3 self)
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This paper utilizes smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences in centripetal acceleration due to vehicle dynamics. These differences combined with angular speed can determine whether the phone is on the left or right side of the vehicle. Our low infrastructure approach is flexible with different turn sizes and driving speeds. Extensive experiments conducted with two vehicles in two different cities demonstrate that our system is robust to real driving environments. Despite noisy sensor readings from smartphones, our approach can achieve a classification accuracy of over 90 % with a false positive rate of a few percent. We also find that by combining sensing results in a few turns, we can achieve better accuracy (e.g., 95%) with a lower false positive rate.
ParkSense: A Smartphone Based Sensing System For On-Street Parking
"... Studies of automotive traffic have shown that on average 30 % of traffic in congested urban areas is due to cruising drivers looking for parking. While we have witnessed a push towards sensing technologies to monitor real-time parking availability, instrumenting on-street parking throughout a city i ..."
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Cited by 13 (0 self)
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Studies of automotive traffic have shown that on average 30 % of traffic in congested urban areas is due to cruising drivers looking for parking. While we have witnessed a push towards sensing technologies to monitor real-time parking availability, instrumenting on-street parking throughout a city is a considerable investment. In this paper, we present ParkSense, a smartphone based sensing system that detects if a driver has vacated a parking spot. ParkSense leverages the ubiquitous Wi-Fi beacons in urban areas for sensing unparking events. It utilizes a robust Wi-Fi signature matching approach to detect driver’s return to the parked vehicle. Moreover, it uses a novel approach based on the rate of change of Wi-Fi beacons to sense if the user has started driving. We show that the rate of change of the observed beacons is highly correlated with actual user speed and is a good indicator of whether a user is in a vehi-cle. Through empirical evaluation, we demonstrate that our approach has a significantly smaller energy footprint than traditional location sensors like GPS and Wi-Fi based posi-tioning while still maintaining sufficient accuracy.
1 Acc: Generic On-Demand Accelerations for Neighbor Discovery in Mobile Applications
"... As a supporting primitive of many mobile device applications, neighbor discovery identifies nearby devices so that they can exchange information and collaborate in a peer-topeer manner. To date, discovery schemes trade a long latency for energy efficiency and require a collaborative duty cycle patte ..."
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Cited by 7 (1 self)
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As a supporting primitive of many mobile device applications, neighbor discovery identifies nearby devices so that they can exchange information and collaborate in a peer-topeer manner. To date, discovery schemes trade a long latency for energy efficiency and require a collaborative duty cycle pattern, and thus they are not suitable for interactive mobile applications where a user is unable to configure others’ devices. In this paper, we propose Acc, which serves as an on-demand generic discovery accelerating middleware for many existing neighbor discovery schemes. Acc leverages the discovery capabilities of neighbor devices, supporting both direct and indirect neighbor discoveries. Our evaluations show that Acc-assisted discovery schemes reduce latency by a maximum of 51.8%, compared with the schemes consuming the same amount of energy. We further present and evaluate a Crowd-Alert application where Acc can be employed by taxi drivers to accelerate selection of a direction with fewer competing taxis and more potential passengers, based on a 10 GB dataset of more than 15,000 taxis in a metropolitan area.
Smartphone sensor reliability for augmented reality applications
"... Abstract. With increasing reliance on the location and orientation sen-sors in smartphones for not only augmented reality applications, but also for meeting government-mandated emergency response requirements, the reliability of these sensors is a matter of great importance. Previous studies measure ..."
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Cited by 6 (1 self)
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Abstract. With increasing reliance on the location and orientation sen-sors in smartphones for not only augmented reality applications, but also for meeting government-mandated emergency response requirements, the reliability of these sensors is a matter of great importance. Previous studies measure the accuracy of the location sensing, typically GPS, in handheld devices including smartphones, but few studies do the same for the compass or gyroscope (gyro) sensors, especially in real-world augmented reality situations. In this study, we measure the reliability of both the location and orientation capabilities of three current gener-ation smartphones: Apple iPhone 4 and iPhone 4s (iOS) phones, as well as a Samsung Galaxy Nexus (Android). Each is tested in three different orientation/body position combinations, and in varying environmental conditions, in order to obtain quantifiable information useful for under-standing the practical limits of these sensors when designing applications that rely on them. Results show mean location errors of 10–30 m and mean compass errors around 10–30◦, but with high standard deviations for both making them unreliable in many settings.
Safe Cities. A Participatory Sensing Approach
- In Proceedings of the 37th IEEE International Conference on Local Computer Networks (LCN
, 2012
"... Abstract—Smart cities combine technology and human re-sources to improve the quality of life and reduce expenditures. Ensuring the safety of city residents remains one of the open problems, as standard budgetary investments fail to decrease crime levels. This work takes steps toward implementing sma ..."
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Cited by 5 (3 self)
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Abstract—Smart cities combine technology and human re-sources to improve the quality of life and reduce expenditures. Ensuring the safety of city residents remains one of the open problems, as standard budgetary investments fail to decrease crime levels. This work takes steps toward implementing smart, safe cities, by combining the use of personal mobile devices and social networks to make users aware of the safety of their surroundings. We propose novel metrics to define location and user based safety values. We evaluate the ability of forecasting techniques including autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) to predict future safety values. We devise iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile device users and integrate our approach into an Android application for visualizing safety levels. We further investigate relationships between location dependent social network activity and crime levels. We evaluate our contributions using data we collected from Yelp as well as crime and census data. I.
