Privacy risk in GeoData: A survey

TL;DR

A taxonomy is proposed to characterise different geomasking techniques proposed to protect individuals' privacy in geodata and serves as a practical resource for data custodians, offering them a means to navigate the extensive array of existing privacy mechanisms and to identify those that align most effectively with their specific requirements.

Abstract

With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. The widespread exposure of such location data poses significant privacy risks to users, as it can lead to re-identification, the inference of sensitive information, and even physical threats. In this survey, we analyse different geomasking techniques proposed to protect individuals' privacy in geodata. We propose a taxonomy to characterise these techniques across various dimensions. We then highlight the shortcomings of current techniques and discuss avenues for future research. Our proposed taxonomy serves as a practical resource for data custodians, offering them a means to navigate the extensive array of existing privacy mechanisms and to identify those that align most effectively with their specific requirements.

Figures (7)

• Figure 1: Fundamental process of address geocoding and reverse address geocoding.
• Figure 2: Taxonomy of geomasking.
• Figure 3: Circular masking with fixed radius (left), Circular masking with random radius (middle), and donut masking (right).
• Figure 4: Gaussian displacement (left), Bimodal Gaussian displacement (middle), and location swapping (right).
• Figure 5: Affine transformation methods, Flipping (left), rotation (middle), and scaling (right).