Towards Understanding Location Privacy Awareness on Geo-Social Networks

Abdelmoty, Alia I.; Alrayes, Fatma S.

doi:10.3390/ijgi6040109

Cited by 10 publications

(5 citation statements)

References 41 publications

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“…Using this tool, the authors aimed to enhance the awareness of information leakage in social networks, particularly fitness apps in which the data retrieved from wearable devices is shared on social networks. Abdelmoty and Alrayes [42] aimed to increase awareness of location privacy on geo-social networks by surveying 186 users, where 77% of them indicated they use location-based services often, several times a day, and 47% of them reported that they were not aware that the locationbased apps collect and store location information even when users select the private location option. Moreover, 43% of respondents were not aware that applications may share location information with third parties.…”

Section: Related Workmentioning

confidence: 99%

Learning Location From Shared Elevation Profiles in Fitness Apps: A Privacy Perspective

Meteriz-Yıldıran

Yildiran

Kim

et al. 2024

IEEE Trans. on Mobile Comput.

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The extensive use of smartphones and wearable devices has facilitated many useful applications. For example, with Global Positioning System (GPS)-equipped smart and wearable devices, many applications can gather, process, and share rich metadata, such as geolocation, trajectories, elevation, and time. For example, fitness applications, such as Runkeeper and Strava, utilize the information for activity tracking and have recently witnessed a boom in popularity. Those fitness tracker applications have their own web platforms and allow users to share activities on such platforms or even with other social network platforms. To preserve the privacy of users while allowing sharing, several of those platforms may allow users to disclose partial information, such as the elevation profile for an activity, which supposedly would not leak the location of the users. In this work, and as a cautionary tale, we create a proof of concept where we examine the extent to which elevation profiles can be used to predict the location of users. To tackle this problem, we devise three plausible threat settings under which the city or borough of the targets can be predicted. Those threat settings define the amount of information available to the adversary to launch the prediction attacks. Establishing that simple features of elevation profiles, e.g., spectral features, are insufficient, we devise both natural language processing (NLP)-inspired text-like representation and computer vision-inspired image-like representation of elevation profiles, and we convert the problem at hand into text and image classification problem. We use both traditional machine learning-and deep learning-based techniques and achieve a prediction success rate ranging from 59.59% to 99.80%. The findings are alarming, highlighting that sharing elevation information may have significant location privacy risks.

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Section: Related Workmentioning

confidence: 99%

Learning Location From Shared Elevation Profiles in Fitness Apps: A Privacy Perspective

Meteriz-Yıldıran

Yildiran

Kim

et al. 2024

IEEE Trans. on Mobile Comput.

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“…Given that users seem to freely share information [ 50 ], the question arises whether they are aware of the consequences. The study by Abdelmoty and Alrayes [ 2 ] indicates that the majority of users does not read privacy policies. Although awareness, especially among younger users, of typical privacy policy components is high, users are often not aware that location data, even if hidden from their social network, for instance, may nevertheless be sent to and stored at the provider if location sensing is not turned off [ 2 ].…”

Section: The Value Of Location Datamentioning

confidence: 99%

“…The study by Abdelmoty and Alrayes [ 2 ] indicates that the majority of users does not read privacy policies. Although awareness, especially among younger users, of typical privacy policy components is high, users are often not aware that location data, even if hidden from their social network, for instance, may nevertheless be sent to and stored at the provider if location sensing is not turned off [ 2 ]. A research area that focusses on this topic is the area of usable privacy [ 187 ].…”

Section: The Value Of Location Datamentioning

confidence: 99%

Location-aware systems or location-based services: a survey with applications to CoViD-19 contact tracking

Schmidtke¹

2020

J Reliable Intell Environ

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With the CoViD-19 pandemic, location awareness technologies have seen renewed interests due to the numerous contact tracking mobile application variants developed, deployed, and discussed. For some, location-aware applications are primarily a producer of geospatial Big Data required for vital geospatial analysis and visualization of the spread of the disease in a state of emergency. For others, comprehensive tracking of citizens constitutes a dangerous violation of fundamental rights. Commercial web-based location-aware applications both collect data and—through spatial analysis and connection to services—provide value to users. This value is what motivates users to share increasingly private and comprehensive data. The willingness of users to share data in return for services has been a key concern with web-based variants of the technology since the beginning. With a focus on two privacy preserving CoViD-19 contact tracking applications, this survey walks through the key steps of developing a privacy preserving context-aware application: from types of applications and business models, through architectures and privacy strategies, to representations.

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“…Under swift and mesmerizing developments in the world of technology and Internet networking, specifically the commercial success of mobile devices, lives of people have become easier and more enjoyable. Location-based services (LBS) form a main part of the Internet of Things (IoT) [1][2][3][4][5][6], where a wide spectrum of IoT applications relies on LBS, including smart cars, wearable devices (smart watches, sleep tracker bracelets, clothes, etc. ), and reward-based LBS applications [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Symbiotic Relationship Based Leader Approach for Privacy Protection in Location Based Services

ALRahhal

Alrahhal

Jamous

et al. 2020

IJGI

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Location-based services (LBS) form the main part of the Internet of Things (IoT) and have received a significant amount of attention from the research community as well as application users due to the popularity of wireless devices and the daily growth in users. However, there are several risks associated with the use of LBS-enabled applications, as users are forced to send their queries based on their real-time and actual location. Attacks could be applied by the LBS server itself or by its maintainer, which consequently may lead to more serious issues such as the theft of sensitive and personal information about LBS users. Due to this fact, complete privacy protection (location and query privacy protection) is a critical problem. Collaborative (cache-based) approaches are used to prevent the LBS application users from connecting to the LBS server (malicious parties). However, no robust trust approaches have been provided to design a trusted third party (TTP), which prevents LBS users from acting as an attacker. This paper proposed a symbiotic relationship-based leader approach to guarantee complete privacy protection for users of LBS-enabled applications. Specifically, it introduced the mutual benefit underlying the symbiotic relationship, dummies, and caching concepts to avoid dealing with untrusted LBS servers and achieve complete privacy protection. In addition, the paper proposed a new privacy metric to predict the closeness of the attacker to the moment of her actual attack launch. Compared to three well-known approaches, namely enhanced dummy location selection (enhanced-DLS), hiding in a mobile crowd, and caching-aware dummy selection algorithm (enhanced-CaDSA), our experimental results showed better performance in terms of communication cost, resistance against inferences attacks, and cache hit ratio.

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Towards Understanding Location Privacy Awareness on Geo-Social Networks

Cited by 10 publications

References 41 publications

Learning Location From Shared Elevation Profiles in Fitness Apps: A Privacy Perspective

Learning Location From Shared Elevation Profiles in Fitness Apps: A Privacy Perspective

Location-aware systems or location-based services: a survey with applications to CoViD-19 contact tracking

A Symbiotic Relationship Based Leader Approach for Privacy Protection in Location Based Services

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