A novel approach of privacy protection of mobile users while using location-based services applications

Saravanan, P. Shanthi; Ramani, Sanjeev Kaushik; Reddy, Vasu; Farhaoui, Yousef

doi:10.1016/j.adhoc.2023.103253

Cited by 25 publications

(1 citation statement)

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“…Additionally, it enables the delivery of localized content to users, enhancing their quality of life [9]. However, challenges arise in directly accessing users' residential location information due to factors such as personal privacy protection and platform policies [10,11]. Consequently, the task of leveraging publicly available social relationship data to infer users' residential locations becomes particularly crucial.…”

Section: Cmes 2024mentioning

confidence: 99%

Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

Zhang

2024

CMES

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Geolocating social media users aims to discover the real geographical locations of users from their publicly available data, which can support online location-based applications such as disaster alerts and local content recommendations. Social relationship-based methods represent a classical approach for geolocating social media. However, geographically proximate relationships are sparse and challenging to discern within social networks, thereby affecting the accuracy of user geolocation. To address this challenge, we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence (NGSI) to improve geolocation accuracy. Firstly, we propose a method for evaluating the homophily of locations based on the k-order neighborhood geographic distribution (k-NGD) similarity among users. There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users. Exploiting this distinction, we filter out non-location-proximate social relationships to enhance location homophily in the social network. To better utilize the location-proximate relationships in social networks, we propose a graph neural network algorithm based on the social structure influence. The algorithm enables us to perform a weighted aggregation of the information of users' multi-hop neighborhood, thereby mitigating the over-smoothing problem of user features and improving user geolocation performance. Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships. Moreover, compared with 7 existing social relationship-based user positioning methods, our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84% to 13.28%.

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Section: Cmes 2024mentioning

confidence: 99%