Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks

Zhu, Liang; Liu, Xiaowei; Jing, Zhiyong; Yu, Liping; Cai, Zengyu; Zhang, Jianwei

doi:10.3390/electronics12010070

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…Zhu et al [26] proposed a knowledge-driven location privacy protection scheme to meet the demand for customized location privacy protection based on users' personalized features. The solution proposes the UBPG algorithm to mine the base persona, model user familiarity and user curiosity, and generate a psychological portrait.…”

Section: Virtual Collaboration Location-based Solutionsmentioning

confidence: 99%

Distributed K-Anonymous Location Privacy Protection Algorithm Based on Interest Points and User Social Behavior

Xing

Zhang

et al. 2023

Electronics

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Location-based services have become an important part of our daily lives, and while users enjoy convenient Internet services, they also face the risk of privacy leakage. K-anonymity is a widely used method to protect location privacy, but most existing K-anonymity location privacy protection schemes use virtual locations to construct anonymity zones, which have the problem of being vulnerable to attackers through background knowledge, while the improved collaborative K-anonymity scheme does not sufficiently consider whether collaborating users share similar attributes. We propose a distributed K-anonymity location privacy-preserving algorithm based on interest points and user social behaviors to solve these problems in existing K-anonymity schemes. The method determines the similarity of users by their interest points and social behaviors and then selects users with high similarity to build an anonymous set of collaborative users. Finally, to ensure the relatively uniform distribution of collaborative users, a homogenization algorithm is used to make the anonymous location points as dispersed as possible. The experimental results showed that our algorithm can effectively resist background attacks, and the uniformly distributed anonymous location points can achieve higher-quality anonymous regions.

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Section: Virtual Collaboration Location-based Solutionsmentioning

confidence: 99%

Distributed K-Anonymous Location Privacy Protection Algorithm Based on Interest Points and User Social Behavior

Xing

Zhang

et al. 2023

Electronics

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“…The evaluation metrics for trajectory clustering include precision, recall, and the F1-score. We utilize location data with semantic tagging, as established in our previous research [40,41], given the absence of an approach to group users within the Geolife datasets into diverse clusters. This experiment aims to compare the performance of trajectory clustering and privacy security for ILP [18], BU [42], SP-tree [43], DP-LTOD [19], N-gram [44], and NPT [45], respectively.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Differential Privacy-Based Spatial-Temporal Trajectory Clustering Scheme for LBSNs

Zhu,

Lei,

et al. 2023

Electronics

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Location privacy preserving for location-based social networks (LBSNs) has been attracting a great deal of attention. Existing location privacy protection methods are disadvantaged by issues such as information leakage and low data availability, which are no longer suitable for the current diverse and personalized location-based services. To address these issues, we propose a differential privacy-based spatial-temporal trajectory clustering (DP-STTC) scheme, which mainly transforms the existing location privacy protection mechanism into a spatial-temporal trajectory protection mechanism by adjusting the privacy parameters. Then, the trajectories were clustered to uncover users with similar trajectory characteristics. Finally, experiments were conducted on two real datasets. The experimental results show that our DP-STTC scheme can not only achieve better accuracy in trajectory clustering, but also protect user privacy.

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“…Faradic et al [4] perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube, and achieved the decay in accuracy when porting models trained in one social media environment to another Takahashi. Zhu et al [15] present a Knowledge-Driven Location Privacy Preserving (KD-LPP) scheme, in order to mine user preferences and provide customized location privacy protection for users. Firstly, the UBPG algorithm is proposed to mine the basic portrait.…”

Section: Introductionmentioning

confidence: 99%

MPCM: Multi-modal User Portrait Classification Model Based on Collaborative Learning

Liu,

2023

ITC

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A social-media user portrait is an important means of improving the quality of an Internet information service. Current user profiling methods do not discriminate the emotional differences of users of different genders and ages on social media against a background of multi-modality and a lack of domain sentiment labels. This paper adopts the sentiment analysis of images and text to improve label classification, incorporating gender and age differences in the sentiment analysis of multi-modal social-media user profiles. In the absence of domain sentiment labels, instance transfer learning technology is used to express the learning method with the sentiment of text and images; the semantic association learning of multi-modal data of graphics and text is realized; and a multi-modal attention mechanism is introduced to establish the hidden image and text. Alignment relationships are used to address the semantic and modal gaps between modalities. A multi-modal user portrait label classification model (MPCM) is constructed. In an analysis of the sentiment data of User users on Facebook, Twitter, and News, the MPCM method is compared with the naive Bayes, Latent Dirichlet Allocation, Tweet-LDA and LUBD-CM(3) methods in terms of accuracy, precision, recall and the FL-score. At a 95% confidence, the performance is improved by 1% to 4% by using the MPCM method.

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Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks

Cited by 4 publications

References 33 publications

Distributed K-Anonymous Location Privacy Protection Algorithm Based on Interest Points and User Social Behavior

Distributed K-Anonymous Location Privacy Protection Algorithm Based on Interest Points and User Social Behavior

Differential Privacy-Based Spatial-Temporal Trajectory Clustering Scheme for LBSNs

MPCM: Multi-modal User Portrait Classification Model Based on Collaborative Learning

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