With the global adoption of smart mobile devices equipped with localization capabilities and broad popularity of microblogging facilities like Twitter, the need for personal privacy has never been greater. This is especially so with computational and data processing infrastructures such as clouds that support big data analysis. Differential privacy of geospatially tagged data such as tweets can potentially ensure that degrees of location privacy can be preserved while allowing the information (tweet contents) to be used for research and analysis, e.g., sentiment analysis.
In this paper, we evaluate differential location pattern-mining approaches considering both privacy and precision of geo-located tweets clustered according to Geo-Locations of Interest (GLI). We consider both the privacy protection strength and the accuracy of results, measuring the Euclidean distance between centroids of real GLIs and obfuscated ones, i.e., those incorporating privacy-preserving noise. We record the performance and sensitivity of the approach. We show how privacy and location precision are trade-offs, i.e., the higher the degree of privacy protection, the fewer the GLIs will be identified. We also quantify these trade-offs and their associated sensitivity levels. We illustrate the work through a big data case study on use of Twitter data for traffic-related data protection.