Author(s)

Wenbing Tang ¹, Weiyuan Zhang ¹

Affiliation(s)

¹ School of Computer Science and Engineering, Anhui University of Science and Technology, Anhui Huainan, China

Corresponding Author

Weiyuan Zhang

ABSTRACT

Location-based services (LBS) are now used in many different industries thanks to the quick growth of mobile computing devices, but this also puts user security and privacy at risk. A location protection system that combines clustering and differential privacy is suggested to solve the issue of uploading and sharing user location information with outside parties. Firstly, the surrounding location points are sorted and divided according to the density of location information, and k-means clustering is used to generalize them; the cluster centers are noise-added by a planar Laplacian mechanism under the premise of satisfying geographic indistinguishability to obtain the perturbed position of each location point, and then location privacy is protected. The experimental results proved that the algorithm in this paper has higher data utilization under the premise of ensuring location privacy.

KEYWORDS

Differential privacy, k - means clustering, location privacy, LBS

CITE THIS PAPER

Wenbing Tang, Weiyuan Zhang. Protecting Privacy through Differential Privacy of Location Data. Advances in Computer, Signals and Systems (2023) Vol. 7: 65-72. DOI: http://dx.doi.org/10.23977/acss.2023.070209.

REFERENCES

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