PCDP-CRLPPM: a classified regional location privacy-protection model based on personalized clustering with differential privacy in data management

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Abstract Location data management plays a crucial role in facilitating data collection and supporting location-based services. However, the escalating volume of transportation big data has given rise to increased concerns regarding privacy and security issues in data management, potentially posing threats to the lives and property of users. At present, there are two possible attacks in data management, namely Reverse-clustering Inference Attack and Mobile-spatiotemporal Feature Inference Attack. Additionally, the dynamic allocation of privacy budgets emerges as an NP-hard problem. To protect data privacy and maintain utility in data management, a novel protection model for location privacy information in data management, Classified Regional Location Privacy-Protection Model based on Personalized Clustering with Differential Privacy (PCDP-CRLPPM), is proposed. Firstly, a twice-clustering algorithm combined with gridding is proposed, which divides continuous locations into different clusters based on the different privacy protection needs of different users. Subsequently, these clusters are categorized into different spatiotemporal feature regions. Then, a Sensitive-priority algorithm is proposed to allocate privacy budgets adaptively for each region. Finally, a Regional-fuzzy algorithm is presented to introduce Laplacian noise into the centroids of the regions, thereby safeguarding users’ location privacy. The experimental results demonstrate that, compared to other models, PCDP-CRLPPM exhibits superior resistance against two specific attack models and achieves high levels of data utility while preserving privacy effectively.