Protecting location privacy: An approach with error-based transformation and mercator projection-based transformation for geospatial vector data

Geospatial data is widely used in various domains like environment management, public safety and location-based services applications. While using vector geospatial data, the significant challenges are related to preserving location privacy. The issues traditional methods face are data exposure to potential breaches, computational complexity, and scalability. Various encryption techniques have recently been proposed to resolve these issues, but they struggle to effectively manage the large volume of geospatial data. In this paper, a novel approach using dual transformation technique is discussed. This dual transformation consists of Error-Based transformation (ERB) followed by Mercator Projection-based transformation to enhance location privacy and address the other challenges. In this approach, the coordinate data stored is first transformed using the ERB approach. Later the Mercator Projection-based transformation is applied to add a layer of security. At the receiver end, the inverse transformation technique is applied to extract the original geospatial coordinate data using the correct set of key parameters. Although the shape of the geospatial object layer is preserved through the proposed methodology, the values are slightly modified, which will disguise the intruder. This transformed data won’t be accurate in terms of the Man in Middle attack. On the other hand, traditional approaches failed to successfully handle the security aspect of vector geospatial data. The security and operational efficiency of the geospatial vector data stored at third-party cloud service providers is achieved. Our solution ensures robustness against potential breaches and unauthorized access to sensitive location details stored on a third-party semi-trusted cloud service provider.