Privacy and the City: User Identification and Location Semantics in Location-Based Social Networks

Rossi, Luca; Williams, Matthew; Stich, Christoph; Musolesi, Mirco

doi:10.1609/icwsm.v9i1.14595

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Zheng et al [44] shows that sharing data that reveals spatiotemporal features of users' mobility patterns on online social networks reveals sensitive information such as home location, using a different form of data, i.e., multimedia. Rossi et al [45] show that location-based social networks are vulnerable to identity privacy breaches by revealing the identity of users by observing their mobility patterns.…”

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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“…Dataset [14] In this study used two dataset for location based social network (LBSN) are:gowalla dataset and foursquare dataset. Gowalla dataset provides API directly access publically available data.…”

mentioning

confidence: 99%

Prediction Future Location for Multiple account users Across Social Network

Abdul Hassan,

Saadoon,

Humadde

2020

J. Al-Qadisiyah Comp. Sci. Math.

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The personal data of people over social networks bring many opportunities for developing a new application. Some users create multiple accounts on social networks. Correlate user information across social networks helps to design algorithms to predict the future location of multiple accounts of a single individual. Most social media site applications need to share their sites with friends and family and this feature has made many people need to predict the site. Prediction is one of the foremost essential issues that need to be investigated for mobility management In mobile computing systems. In this paper, a proposed algorithm for future location prediction of multiple account users based on using frequent pattern mining. First, apply the frequency algorithm. The proposed algorithm experimented using "Gowalla and foursquare datasets" were presented simple method can be used to infer the user location using publicly available attributes and also the geographic information associated with allocatable friends. We find that it is possible to infer the user city with high accuracy. The proposed algorithm prediction achieved accuracy 96% in the Gowalla dataset when applying the support and confidence for each region that visited the user in a specific time period through some years or many months.

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Crowd-enabled Processing of Trustworthy, Privacy-Enhanced and Personalised Location Based Services with Quality Guarantee

Hashem

Hasan

Salim

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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We propose a novel approach for enabling trustworthy, privacy-enhanced and personalised location based services (LBSs) that find nearby points of interests (POIs) such as restaurants, ATM booths, and hospitals in a crowdsourced manner. In our crowdsourced approach, a user forms a group from the crowd and processes the LBS using the POI knowledge of the group members without involving an external service provider. We use personalised rating in addition to the distance of a POI for finding the answers of the location based queries. The personalised rating of a POI is computed using individual POI ratings given by the group members and the query requestor's trust and similarity scores for the group members. The major challenges for the crowdsourced data are incompleteness and inaccuracy, which may result in lower quality answer for the LBS. In this paper, we first present techniques to select knowledgeable group members for processing LBSs and thereby increase the accuracy and the confidence level of the query answers. We then develop efficient algorithms to process LBSs in real time and enhance privacy by reducing the number of the group members' POIs shared with the query requestor. Finally, we run extensive experiments using real datasets to show the efficiency and effectiveness of our approach.

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Privacy and the City: User Identification and Location Semantics in Location-Based Social Networks

Cited by 4 publications

References 26 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

Prediction Future Location for Multiple account users Across Social Network

Crowd-enabled Processing of Trustworthy, Privacy-Enhanced and Personalised Location Based Services with Quality Guarantee

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