The Perils of User Tracking Using Zero-Permission Mobile Apps

Narain, Sashank; Vo-Huu, Triet D.; Block, Kenneth; Noubir, Guevara

doi:10.1109/msp.2017.25

Cited by 11 publications

(10 citation statements)

References 6 publications

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“…Most of these works exploits IMU sensors like accelerometer and gyroscope or the traces from other sensors like GPS and acoustic for fine grained mobility context (say, vehicle type) detection [15], [16]. However, despite all these successes in detecting the mobility just by using IMU sensors and GPS information, there has been a consistent criticism of these sensors for leaking out private information regarding the location of users [7]. Most of these works concentrate on detecting the mobility first and then subsequently use the mobility along with the spatiotemporal information to predict the location of the user [4], [8].…”

Section: Related Workmentioning

confidence: 99%

“…Subsequently, several research works have come up with proper definitions and threat models that deal with such privacy breaches involving leakage of location information [5]- [7]. However, a common approach suggested by most of these works is in devising policies that restrict the usage of IMU sensors.…”

Section: Related Workmentioning

confidence: 99%

“…With smartphone usage becoming more and more prevalent, several independent research works have started looking into the potential sources that can breach the location privacy of a user [5]. Most of these research works point at one common source of such leakage -the IMU sensors [6], [7]. Although, IMU sensors are explicitly used to detect the mobility context of a user accurately, however, accurate mobility information coupled with spatio-temporal information can disclose the approximate location of a user at different times of the day [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Mobility Context over Smartphones using Typing and Smartphone Engagement Patterns

Chatterjee

Bhowmik

Singh

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Most of the latest context-based applications capture the mobility of a user using Inertial Measurement Unit (IMU) sensors like accelerometer and gyroscope which do not need explicit user-permission for application access. Although these sensors provide highly accurate mobility context information, existing studies have shown that they can lead to undesirable leakage of location information. To evade this breach of location privacy, many of the state-of-the-art studies suggest to impose stringent restrictions over the usage of IMU sensors. However, in this paper, we show that typing and smartphone engagement patterns can act as an alternative modality to sniff the mobility context of a user, even if the IMU sensors are not sampled at all. We develop an adversarial framework, named ConType, which exploits the signatures exposed by typing and smartphone engagement patterns to track the mobility of a user. Rigorous experiments with in-the-wild dataset show that ConType can track the mobility contexts with an average micro-F 1 of 0.87 (±0.09), without using IMU data. Through additional experiments, we also show that ConType can track mobility stealthily with very low power and resource footprints, thus further aggravating the risk.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Mobility Context over Smartphones using Typing and Smartphone Engagement Patterns

Chatterjee

Bhowmik

Singh

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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“…In designing such tools and APIs, openness of data and analytics services hereby need to be balanced with data privacy, as these may be suspect to location-based data mining attempts/attacks (e.g., see [65,66]). …”

Section: Implication For Future Geospatial Game Analytics Platformsmentioning

confidence: 99%

On Metrics for Location-Aware Games

Rodríguez-Pupo

Casteleyn

Granell

2017

IJGI

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Abstract:Metrics are important and well-known tools to measure users' behavior in games, and gameplay in general. Particularities of location-aware games-a class of games where the player's location plays a central role-demand specific support in metrics to adequately address the spatio-temporal features such games exhibit. In this article, we analyse and discuss how existing game analytics platforms address the spatio-temporal features of location-aware games. Our analysis reveals that little support is available. Next, based on the analysis, we propose a classification of spatial metrics, embedded in existing literature, and discuss three types of spatial metrics-point-, trajectory-and area-based metrics-, and elaborate examples and difficulties. Finally, we discuss how spatial metrics may be deployed to improve gameplay in location-aware games.

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“…Finally, and hand in hand with custom event types as discussed in the previous point, the future geospatial analytics platform should support custom geospatial computation and custom metrics specification and execution at the server side, accompanied by convenient tools and APIs to support developers in handling the full metrics life-cycle (i.e., definition, execution, spatial analysis, interpretation of results). In designing such tools and APIs, openness of data and analytics services hereby need to be balanced with data privacy, as these may be suspect to location-based data mining attempts/attacks (e.g., see Narain et al (2017); Jia et al (2015)).…”

Section: Implication For Future Geospatial Game Analytics Platformsmentioning

confidence: 99%

An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics in Location-aware Games

Pupo¹,

Enrique²

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On the other hand, infrastructure-related enablers are now in place: fully rolledout communication networks (e.g., 3G and 4G) with resulting ubiquitous internet access, and commercially available, economised cloud-based storage and computing infrastructures (Varghese and Buyya, 2018), provide unprecedented means to build the next-generation of context-aware applications and services, based on multi-user, real-time and high-frequency input streams; real-time data handling, processing and analytics; and real-time, location-and context-based reactiveness (Bainomugisha et al., 2013). Indeed, we see a breakthrough of such applications in various application fields, such as mobility and transportation (e.g., Wan et al. (2014)), health (e.g., Solanas et al. (2014); Chang et al. (2017)), tourism (e.g., Meehan et al. (2013), smart cities (e.g., García et al. (2017); Sagl et al. (2015)), smart homes (e.g., Alirezaie et al. (2017)), gaming (e.g., Pokemon Go (Andone et al., 2017), to name but a few.Nevertheless, due to their relative new and evolving supportive technologies, building such applications is yet a laborious job. The client-side application needs to deal with and be built around an additional, dynamically changing concern, namely context in general and location specifically, while server-side handling of context data, especially in large-scale multi-user deployments, needs to deal with streaming data, big data issues, spatial analysis and reactiveness.In this technological context, where context information is highly available, different types of applications leverage and combine sensor data with data regarding social aspects to provide both services and entertainment in unprecedented ways. A type of context-aware, location based application that is gaining popularity worldwide are location-aware games. Location-aware games are games that take place in the real world space, using the location as an important aspect of the game, and often, but not always, require the user to move physically in space (see Chapter 2). Digitally-enabled variants of these games often rely on the capture and analysis of context data produced and processed continuously during the game development, and the user expects the outputs driving the game to be produced in a timely manner. In such location-aware games, stream computing will become a vital feature for multiple reasons. First, it will allow proper management of huge volumes of incoming, varied data that these location-aware games

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The Perils of User Tracking Using Zero-Permission Mobile Apps

Cited by 11 publications

References 6 publications

Detecting Mobility Context over Smartphones using Typing and Smartphone Engagement Patterns

Detecting Mobility Context over Smartphones using Typing and Smartphone Engagement Patterns

On Metrics for Location-Aware Games

An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics in Location-aware Games

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