On the Impact of Location Errors on Localization Attacks in Location-Based Social Network Services

Cheng, Hanni; Mao, Shiling; Xue, Minhui; Hei, Xiaojun

doi:10.1007/978-3-319-49148-6_29

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Xue et al [19] analyze the feature that WeChat report relative distance between nearby users in bands and propose a localization algorithm, which study the localization of the target on a line, then extended it to two-dimensional space. Based on Xue's work, Cheng et al [20] point out that the initial probe's location will affect the localization accuracy of one-dimensional method and propose a new deployment strategy of the initial probe. Considering that the LBSD service will confuse the nearby user's distance, Peng et al [21] propose a two-dimensional localization method based on heuristic number theory, which further improves the practicality.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Geolocation Method for Malicious LBSD Users Based on Dynamic Adjustment of Probes

Shi¹,

Luo²,

Guo³

et al. 2022

Wireless Communications and Mobile Computing

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The integration of the Internet of Things (IoT) and social networks is a promising trend of network technology. However, the diversity of social networks also poses potential risks to IoT security. Researching on the geolocation of social network users can verify the effectiveness of location protection mechanisms adopted by service providers, as well as provide a means for geolocating miscreants in social networks. Most current research focuses on how to infer the true location of a target within a specific region, such as within a city, while less research has been done on how to achieve fast and accurate localization of targets under long-range conditions. In this manuscript, an efficient localization method for LBSD users at long distances based on dynamic adjustment of probes (DAPL) is proposed. Based on the analysis of factors that affect the accuracy and efficiency of the target location approximation, DAPL can approach the real location sustainability of the target by dynamically generating probe locations. By identifying abnormal fluctuations of the target’s reported distance, timely corrections of probe location are made to improve efficiency. In experimental results for Momo, a global popular LBSD social platform with more than 115 million active users show that even the initial probe is thousands of miles away from the target, DAPL can geolocate the target with a success rate close to 100% (99.5%), which is much higher than 70.6% of the existing method. Only about 12 times of LBSD service queries are needed, and DAPL can geolocate 88.9% of targets within 40 meters with an average error of 22.1 meters, which has higher efficiency and approximate accuracy compared with the existing typical method.

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Section: Introductionmentioning

confidence: 99%

An Efficient Geolocation Method for Malicious LBSD Users Based on Dynamic Adjustment of Probes

Shi¹,

Luo²,

Guo³

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…By deploying multiple probes along the horizontal and the vertical edge, middle probes which get the minimum reported distance in each edge are selected as the predicted coordinates. Cheng et al 28 pointed out that the existence of noise makes geolocation errors of number theory based algorithm increase as the actual distance between the probes and the target user increases. In addition, placement strategies for the first probe were proposed to improve the practicality of the number theory-based geolocation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Geolocating a WeChat user based on the relation between reported and actual distance

Shi

Luo

Zhao

et al. 2018

International Journal of Distributed Sensor Networks

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The combination of social networks and the Internet of Things has raised a new wave of network technology application. However, the presence of malicious social network users poses a potential threat to Internet of Things security, and the research on social network user geolocation technology is of great significance. The accuracy of existing geolocation methods for WeChat users depends on the stable correspondence between the reported distance and actual distance. In view of the difficulty to pinpoint users' location in the real world due to WeChat location protection strategy, a WeChat user geolocation algorithm based on the reported and actual distance relation analysis is proposed. The proposed algorithm selects the target reported distance and determines the initial target space based on statistical characteristics of the relation between the reported distance and actual distance. What is more, stepwise strategies are taken to improve the accuracy rate of space partition. Experimental results show that, on the premise that target users can be discovered, the proposed algorithm could achieve higher accuracy compared with the classical space partition-based algorithm and the heuristic number theory based algorithm. The highest geolocation accuracy is within 10 m and 56% of geolocation results are within 60 m.

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WeChat User Geolocation Method Based on Probe Deployment Optimization and the User Order

Yang,

Shi,

Wei

2024

Mechanisms and Machine Science

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On the Impact of Location Errors on Localization Attacks in Location-Based Social Network Services

Cited by 6 publications

References 10 publications

An Efficient Geolocation Method for Malicious LBSD Users Based on Dynamic Adjustment of Probes

An Efficient Geolocation Method for Malicious LBSD Users Based on Dynamic Adjustment of Probes

Geolocating a WeChat user based on the relation between reported and actual distance

WeChat User Geolocation Method Based on Probe Deployment Optimization and the User Order

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