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Enhancing Security in Geographic Information Systems: Anonymization and Differential Privacy Techniques for Protecting Sensitive Geospatial Data

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  • Rahul Marri

  • Sriram Varanasi

  • Satwik Varma Kalidindi Chaitanya

  • Sai Krishna Marri

Abstract

As Geographic Information Systems (GIS) increasingly facilitate the analysis and sharing of geospatial data, the protection of sensitive information becomes paramount. This research explores the implementation of anonymization and differential privacy techniques to enhance security in GIS. Anonymization methods effectively remove or obscure personally identifiable information from geospatial datasets, while differential privacy introduces a mathematical framework that allows for the sharing of aggregate data without compromising individual privacy. This study evaluates the strengths and weaknesses of these techniques, demonstrating their effectiveness in maintaining the utility of geospatial data while safeguarding sensitive information. Through case studies and comparative analysis, we provide insights into best practices for integrating these privacy-preserving strategies into GIS applications, ensuring compliance with legal regulations and fostering public trust in geospatial technologies.

Suggested Citation

  • Rahul Marri & Sriram Varanasi & Satwik Varma Kalidindi Chaitanya & Sai Krishna Marri, 2024. "Enhancing Security in Geographic Information Systems: Anonymization and Differential Privacy Techniques for Protecting Sensitive Geospatial Data," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 5(1), pages 469-482.
    • Handle: RePEc:das:njaigs:v:5:y:2024:i:1:p:469-482:id:240
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    1. Venkata Mohit Tamanampudi, 2024. "CoWPE: Adaptive Context Window Adjustment in LLMs for Complex Input Queries," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 5(1), pages 438-450.
    2. Ankit Sharma, 2024. "The Development of an Automated Approach for Designing Quantum Algorithms Using Circuits Generated By Generative Adversarial Networks (Gans)," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 4(1), pages 1-140.
    3. Ankit Sharma, 2024. "Bridging Paradigms: The Integration of Symbolic and Connectionist AI in LLM-Driven Autonomous Agents," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 138-150.
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