Strengthening GIS Security: Anonymization and Differential Privacy for Safeguarding Sensitive Geospatial Data

Rahul Marri (), Sriram Varanasi (), Satwik Varma Kalidindi Chaitanya () and Sai Krishna Marri ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 4, issue 1, 338-361

Abstract: The protection of Geographic Information Systems (GIS) is now more relevant since these systems gather, process, and store geospatial data to various ends, receiving and processing a broad array of applications. Data in the GIS framework is open to everyone, and digital assault, cyber theft, and many more issues which make privacy important. This paper addresses two methods: anonymization and differential privacy to protect GIS data. The performance of anonymization techniques like k-anonymity and geo-indistinguishability and the ability of differential privacy techniques to prevent the reverse engineering of the original data in large datasets are assessed. An area of interest to the research is the applicability of these techniques in reducing the threat of traditional GIS security threats. The paper uses several cases and quantitative evaluation of the results to describe the advantages and disadvantages of both types of analysis and to demonstrate how these analyses can be applied in practice. These methods show that data breaches are minimized and general data protection improved by as much as 30% for location-specific attacks, for instance. This research seeks to address the application of privacy-preserving techniques in the GIS while requiring high privacy standards in using geospatial datasets. Importantly, the study's findings are intended to inform policymakers and system designers of the best practices for improving GIS security structures.

Keywords: GIS; Geospatial; Data Privacy; GDPR; Geographic; Data Breaches; Anonymization (search for similar items in EconPapers)
Date: 2024
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