NRand-K: Minimizing the impact of location obfuscation in spatial analysis

Location privacy, or geoprivacy, is critical to secure users’ privacy in context-aware applications. Location-based services pose privacy risks for users, due to the inferences that could be made about them from their location information and the potential misuse of this data by service providers or third-party companies. A common solution is to apply masking or location obfuscation, which degrades location information quality while keeping a geographic coordinate structure. However, there is a trade-off between privacy, quality of service, and quality of information, the last one being a valuable asset for companies. NRand is a location privacy mechanism with obfuscation capabilities and resistance against filtering attacks. In order to minimize the impact on location information quality, NRand-K has been introduced. This algorithm is designed for use when releasing location information to third parties or as open data with privacy concerns. To assess the impact of location obfuscation on exploratory spatial data analysis (ESDA), a comparison is performed between obfuscated data with NRand, NRand-K, and unaltered data. For the experiments, geolocated tweets collected during the Central Italy 2016 earthquake are used. Results show that NRand-K reduces the impact on ESDA, where inferences were similar to those obtained with the unaltered data.