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.

Social media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. In these critical situations, it is not surprising that privacy is often considered a secondary problem. In order to prevent subsequent abuse, theft or public exposure of collected datasets, however, protecting the privacy of social media users is crucial. Avoiding unnecessary data retention is an important question that is currently largely unsolved. There are a number of technical approaches available, but their deployment in disaster management is either impractical or requires special adaption, limiting its utility. In this case study, we explore the deployment of a cardinality estimation algorithm called HyperLogLog into disaster management processes. It is particularly suited for this field, because it allows to stream data in a format that cannot be used for purposes other than the originally intended. We develop and conduct a focus group discussion with teams of social media analysts. We identify challenges and opportunities of working with such a privacy-enhanced social media data format and compare the process with conventional techniques. Our findings show that, with the exception of training scenarios, deploying HyperLogLog in the data acquisition process will not distract the data analysis process. Instead, several benefits, such as improved working with huge datasets, may contribute to a more widespread use and adoption of the presented technique, which provides a basis for a better integration of privacy considerations in disaster management.

Social media data is widely used to gain insights about social incidents, whether on a local or global scale. Within the process of analyzing and evaluating the data, it is common practice to download and store it locally. Considerations about privacy protection of social media users are often neglected thereby. However, protecting privacy when dealing with personal data is demanded by laws and ethics. In this paper we introduce a method to store social media data using the cardinality estimator HyperLogLog. Based on an exemplary disaster management scenario, we show that social media data can be analyzed by counting occurrences of posts, without becoming in possession of the actual raw data. For social media data analyses like these, that are based on counting occurrences, cardinality estimation suffices the task. Thus, the risk of abuse, loss or public exposure of the data can be mitigated and privacy of social media users can be preserved. The ability to do unions and intersections on multiple data sets further encourages the use of this technology. We provide a proof-of-concept implementation for our introduced method, using data provided by the Twitter API.

Social media data are widely used to gain insights about social incidents, whether on a local or global scale. Within the process of analyzing and evaluating the data, it is common practice to download and store it locally. Considerations about privacy protection of social media users are often neglected thereby. However, protecting privacy when dealing with personal data is demanded by laws and ethics. In this paper, we introduce a method to store social media data using the cardinality estimator HyperLogLog. Based on an exemplary disaster management scenario, we show that social media data can be analyzed by counting occurrences of posts, without becoming in possession of the actual raw data. For social media data analyses like these, that are based on counting occurrences, cardinality estimation suffices the task. Thus, the risk of abuse, loss, or public exposure of the data can be mitigated and privacy of social media users can be preserved. The ability to do unions and intersections on multiple datasets further encourages the use of this technology. We provide a proof-of-concept implementation for our introduced method, using data provided by the Twitter API.