Privacy-Aware Visualization of Volunteered Geographic Information (VGI) to Analyze Spatial Activity: A Benchmark Implementation

Abstract

Through volunteering data, people can help assess information on various aspects of their surrounding environment. Particularly in natural resource management, Volunteered Geographic Information (VGI) is increasingly recognized as a significant resource, for example, supporting visitation pattern analysis to evaluate collective values and improve natural well-being. In recent years, however, user privacy has become an increasingly important consideration. Potential conflicts often emerge from the fact that VGI can be re-used in contexts not originally considered by volunteers. Addressing these privacy conflicts is particularly problematic in natural resource management, where visualizations are often explorative, with multifaceted and sometimes initially unknown sets of analysis outcomes. In this paper, we present an integrated and component-based approach to privacy-aware visualization of VGI, specifically suited for application to natural resource management. As a key component, HyperLogLog (HLL)—a data abstraction format—is used to allow estimation of results, instead of more accurate measurements. While HLL alone cannot preserve privacy, it can be combined with existing approaches to improve privacy while, at the same time, maintaining some flexibility of analysis. Together, these components make it possible to gradually reduce privacy risks for volunteers at various steps of the analytical process. A specific use case demonstration is provided, based on a global, publicly-available dataset that contains 100 million photos shared by 581,099 users under Creative Commons licenses. Both the data processing pipeline and resulting dataset are made available, allowing transparent benchmarking of the privacy–utility tradeoffs.

Visualizing Point Density on Geometry Objects: Application in an Urban Area Using Social Media VGI

“…In this regard, the resulting dataset of our approaches provides data privacy because the information on single points is lost by being aggregated within density surfaces that are assigned to the geometry objects. We also foresee the possibility of adapting the models of our approaches for processing privacy-aware data format proposed by Dunkel et al (2020) as long as the spatial precision is applicable to the used geometry.…”

Analyzing the EU Migration Crisis as Reflected on Twitter

“…Additionally, the hashing also uses a secret salt (Kaliski 2000) which substantially improves the security of hashed elements. The detailed software architecture which realizes these steps can be found in Dunkel et al (2020). Furthermore, increasing spatial granularity of location data by using GeoHash (Dunkel et al 2020), the format reduces risks by following geoprivacy-by-design recommendations (Kounadi et al 2018).…”

Emojis as Contextual Indicants in Location-Based Social Media Posts

“…In order to ensure a privacy-aware use of this dataset, an integrated and componentbased approach was used [43], based on the data abstraction format HyperLogLog (HLL), first described by [44]. In summary, during data retrieval, emojis are extracted from posts and quantitative measurements (post counts, user counts) are stored as approximate HLL sets for each distinct emoji.…”