High variability geographical obfuscation for location privacy

In the last few years, the number of applications that use the location of the users in order to provide a more personalized service have been increasing, mainly due to easy access to low cost smartphones, geographical positioning systems and other factors, like social networking. The concern of protecting this information is also increasing due to the capabilities than an eventual attacker could have if the location information is obtained. Some protection techniques have been proposed in the literature; for example, location obfuscation which slightly alters the location to hide the real one. However, this technique could be filtered out with time series-based mechanisms. In this work, the Pinwheel obfuscation technique is proposed in order to reduce the possibility of EMA-based filtering based on high variability and asymmetry of the induced noise. The results show that the level of filtered noise is reduced from 35% in N-RAND and 30% in θ-Rand obfuscation techniques, to 15% in Pinwheel, with asymmetric scenarios, while preserving a long final average distance from the original path after a filtering attack.