On the validity of geosocial mobility traces

Zhang, Zengbin; Zhou, Lin; Zhao, Xiaohan; Gang, Wang; Su, Yu; Metzger, Miriam J.; Zheng, Haitao; Zhao, Ben Y.

doi:10.1145/2535771.2535786

Cited by 45 publications

(16 citation statements)

References 31 publications

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“…This could allow a deep investigation into cross-site information aggregation. For example, we can reconstruct a user's mobility pattern [24] by aggregating the detailed geographic data from different OSN sites.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, a Foursquare user can leave a "tip" for any venue, which is a publicly viewable comment on the venue according to the user's experience. There has been existing measurement work on Foursquare [14,10,24].…”

Section: Overview Of Foursquare and Its Cross-site Linking Functionmentioning

confidence: 99%

“…Given the large user population, previous work only uses a small subset of users to conduct data-driven studies related to Foursquare [14,10,24]. In our work, we aim to analyze the entire Foursquare user base, and this can avoid the disadvantages of biased sampling.…”

Section: Data Collectionmentioning

confidence: 99%

“…different functionality. A person might use Facebook to interact with friends [21], use Twitter to share breaking news [8], use LinkedIn for job search, and use Foursquare for location-centric social applications [14,10,24]. It is quite common for an individual user to have multiple accounts on different OSN sites.…”

Section: Introductionmentioning

confidence: 99%

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Understanding Cross-site Linking in Online Social Networks

Yang

Zhuang

Cao

et al. 2014

Proceedings of the 8th Workshop on Social Network Mining and Analysis

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Online social networks (OSNs) have attracted billions of users, and play an important role in people's daily life. A user often has accounts on multiple OSN sites. In this paper, we study the emerging "cross-site linking" function, which is supported by many OSNs. Our study is based on Foursquare, a representative location-based social networking (LBSN) service. We conduct a data-driven analysis by using crawled public profiles of almost all (if not all) Foursquare users. Our analysis has shown that the crosssite linking function is widely adopted by Foursquare users, and the users who have enabled this function are more active than other users. We have also found that users who are more concerned with online privacy have a lower probability to enable the cross-site linking function. Besides analyzing crawled Foursquare user profiles, we further explore cross-site linking between Foursquare and other OSN sites, i.e., Facebook and Twitter. The study on "FoursquareFacebook" linking indicates that users have a high probability to provide consistent information to different OSNs. Meanwhile, "Foursquare-Twitter" linking is used to demonstrate the usefulness of aggregating user-generated content across multiple OSN sites. We present a gender-based analysis of Twitter, which is made accurate by leveraging crosssite links between Foursquare and Twitter.

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Section: Discussionmentioning

confidence: 99%

Section: Overview Of Foursquare and Its Cross-site Linking Functionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding Cross-site Linking in Online Social Networks

Yang

Zhuang

Cao

et al. 2014

Proceedings of the 8th Workshop on Social Network Mining and Analysis

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“…According to recent measurement studies [39], [41], 39.9% check-ins (on Foursquare) are remote check-ins with over 500 meters away from users' actual GPS location. Users often check-in at a remote location (that they are not physically visiting) to earn virtual badges or compete with their friends.…”

Section: Possible Reasons Behind the Mismatchesmentioning

confidence: 99%

De-anonymization of Mobility Trajectories: Dissecting the Gaps between Theory and Practice

Wang

Gao

Yong

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

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Abstract-Human mobility trajectories are increasingly collected by ISPs to assist academic research and commercial applications. Meanwhile, there is a growing concern that individual trajectories can be de-anonymized when the data is shared, using information from external sources (e.g. online social networks). To understand this risk, prior works either estimate the theoretical privacy bound or simulate de-anonymization attacks on synthetically created (small) datasets. However, it is not clear how well the theoretical estimations are preserved in practice.In this paper, we collected a large-scale ground-truth trajectory dataset from 2,161,500 users of a cellular network, and two matched external trajectory datasets from a large social network (56,683 users) and a check-in/review service (45,790 users) on the same user population. The two sets of large ground-truth data provide a rare opportunity to extensively evaluate a variety of de-anonymization algorithms (7 in total). We find that their performance in the real-world dataset is far from the theoretical bound. Further analysis shows that most algorithms have underestimated the impact of spatio-temporal mismatches between the data from different sources, and the high sparsity of user generated data also contributes to the underperformance. Based on these insights, we propose 4 new algorithms that are specially designed to tolerate spatial or temporal mismatches (or both) and model user behavior. Extensive evaluations show that our algorithms achieve more than 17% performance gain over the best existing algorithms, confirming our insights.

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A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

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Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

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On the validity of geosocial mobility traces

Cited by 45 publications

References 31 publications

Understanding Cross-site Linking in Online Social Networks

Understanding Cross-site Linking in Online Social Networks

De-anonymization of Mobility Trajectories: Dissecting the Gaps between Theory and Practice

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

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