A Review on Privacy-Preserving Techniques for Spatiotemporal Data

Abstract

The rapid proliferation of spatiotemporal data—originating from GPS-enabled devices, sensor networks, IoT ecosystems, and social media platforms—has raised critical privacy concerns. Protecting sensitive information while preserving data utility is increasingly becoming a central research challenge. Although existing surveys have addressed privacy-preserving methods, many lack systematic methodological frameworks, rigorous quantitative comparisons, or in-depth analysis of emerging techniques tailored to spatiotemporal contexts. This survey bridges these gaps by offering a comprehensive and structured review of privacy-preserving techniques specific to spatiotemporal data. Our contributions include: (i) a systematic methodology for selecting and evaluating relevant literature from reputable sources, (ii) a novel theoretical classification of techniques including data anonymization, differential privacy, cryptographic methods, federated learning, and geospatial watermarking, (iii) a quantitative comparison based on standardized performance metrics, and (iv) a critical analysis of hybrid approaches that integrate differential privacy, cryptography, and machine learning. Furthermore, the paper highlights current limitations, practical challenges, and key areas for future research, serving as a roadmap for researchers and practitioners. The insights provided aim to foster the development of robust, scalable, and privacy-aware solutions for spatiotemporal data analysis.