A Dummy Location Selection Algorithm Based on Location Semantics and Physical Distance

Yang, Dongdong; Ye, Baopeng; Chen, Yuling; Zhou, Huiyu; Qian, Xiaobin

doi:10.1007/978-3-030-93206-0_17

Cited by 4 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It first selected these virtual locations based on the entropy metric and subsequently introduced an improved DLS algorithm to maximize the dispersion of these selected virtual locations. Based on this, Yang et al [38] took query probability and semantic location information as critical parameters, presented a virtual location selection model to evaluate the quality of virtual locations, and used a genetic algorithm-based optimization method to find the optimal solution, which ends up with a set of virtual locations with query probability as close as possible and also makes the locations in them as semantically and physically dispersed as possible. Unlike the above, the paper [39] built a semantic location tree (LST) and converted the semantic distance into the number of hops between nodes in the LST, designing a method that takes into account the semantic diversity and physical dispersion of locations, and combined the two objectives of geographic location and location semantics into a single objective optimization problem to improve efficiency, and ultimately produced an anonymous region.…”

Section: Discrete Point-based K-anonymizationmentioning

confidence: 99%

A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities

Shen,

Wang,

Zhang

2023

Sensors

View full text Add to dashboard Cite

With the popularity of location services and the widespread use of trajectory data, trajectory privacy protection has become a popular research area. k-anonymity technology is a common method for achieving privacy-preserved trajectory publishing. When constructing virtual trajectories, most existing trajectory k-anonymity methods just consider point similarity, which results in a large dummy trajectory space. Suppose there are n similar point sets, each consisting of m points. The size of the space is then mn. Furthermore, to choose suitable k− 1 dummy trajectories for a given real trajectory, these methods need to evaluate the similarity between each trajectory in the space and the real trajectory, leading to a large performance overhead. To address these challenges, this paper proposes a k-anonymity trajectory privacy protection method based on the similarity of sub-trajectories. This method not only considers the multidimensional similarity of points, but also synthetically considers the area between the historic sub-trajectories and the real sub-trajectories to more fully describe the similarity between sub-trajectories. By quantifying the area enclosed by sub-trajectories, we can more accurately capture the spatial relationship between trajectories. Finally, our approach generates k−1 dummy trajectories that are indistinguishable from real trajectories, effectively achieving k-anonymity for a given trajectory. Furthermore, our proposed method utilizes real historic sub-trajectories to generate dummy trajectories, making them more authentic and providing better privacy protection for real trajectories. In comparison to other frequently employed trajectory privacy protection methods, our method has a better privacy protection effect, higher data quality, and better performance.

show abstract

Section: Discrete Point-based K-anonymizationmentioning

confidence: 99%

A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities

Shen,

Wang,

Zhang

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In order to prevent the leakage of privacy information, a variety of location privacy protection methods is proposed by experts and scholars, including the k ‐anonymity method, 13 dummy location method 14 and encryption method 15 . Among these location privacy protection methods, k ‐anonymity 16 method was born in the relational database, and its key attribute is dealt with using a generalization and fuzzy technology.…”

Section: Related Workmentioning

confidence: 99%

A location privacy protection method based on cipher‐text retrieval in cloud environment

Zhang

Jiang

et al. 2022

Security and Privacy

View full text Add to dashboard Cite

In order to improve the efficiency of location query and the effect of privacy protection in cloud environment, a location privacy protection method based on cipher-text retrieval was proposed. Firstly, the spatial area is divided into several square grids, and the big Morton code of each location is calculated, the spatial area in the grid is divided again, and the small Morton code of each location is calculated. Secondly, the location data is encrypted by the combination of public key encryption and scrambling encryption, and then outsourced to the cloud server. When querying the location data of the interest points, the query request is sent to the data owner. After obtaining the query trapdoor and the private key, the query trapdoor is sent to the location data server for query. On the server side, the interest points in the region are found according to the cipher-text matching, and the cipher-text top-k sorting is implemented. Finally, the cipher-text query results are decrypted into plaintext by the private key. Experimental results show that the proposed method ensures the location data privacy, and improves query efficiency. At the same time, the balance between privacy protection security and query quality is achieved.

show abstract

“…However, the scheme ignores the dispersion of the generated dummy locations and therefore suffers from the danger of location homogeneity attack. 24 Wang et al 25 proposed a maximum and minimum dummy location selection algorithm based on location semantics and query probability, which integrates both location semantics and query probability, and also considers dispersion, quantifying location semantics through computation, but the way of measuring semantic differentiation is crude, the result is not accurate enough, the semantics do not apply widely enough, and there are deficiencies.…”

Section: Introductionmentioning

confidence: 99%

Research on privacy protection of dummy location interference for Location-Based Service location

2022

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Location privacy refers to the individual private and sensitive location information involved in the user’s access to location services. Achieving location privacy protection has become a hot topic of research. However, existing location privacy protection schemes are susceptible to background knowledge attack, edge information attack, and homogeneity attack, on the one hand, and strict constraint on the number of neighbors, on the other hand. To address these deficiencies, a dummy location interference privacy protection algorithm for Location-Based Service location is proposed. To begin with, the dummy location candidate set is constructed based on using WordNet structure to guarantee semantic differentiation, randomly selecting offset location, and conforming to probability similarity; next, the dummy location set is filtered out by discretizing dummy locations based on the Heron formula; finally, the secure anonymity set is constructed according to the anonymity level. Experiments show that the algorithm enhances the privacy protection strength and improves the security of location privacy. Meanwhile, the communication volume and time overhead are reduced and the practicality is boosted by taking into account the sparse and dense environment of location points.

show abstract

A Dummy Location Selection Algorithm Based on Location Semantics and Physical Distance

Cited by 4 publications

References 16 publications

A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities

A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities

A location privacy protection method based on cipher‐text retrieval in cloud environment

Research on privacy protection of dummy location interference for Location-Based Service location

Contact Info

Product

Resources

About