Location l-Diversity against Multifarious Inference Attacks
Shinya Miyakawa, Nobuyuki Saji, Takuya Mori · 2012
The increasing use of location-based services leads these users to publish their locations unintentionally. Adversarial attackers can identify the user and find sensitive locations where the user often visits. Although l-diversity can be applied to location data and protect the user's privacy by preserving variations of locations, it does not consider the difference of the adversary's knowledge and does not properly address each sensitive location. The sensitive locations vary depending on the adversary's knowledge which reflects the relationship between the adversary and the user. In this paper, we introduce multi-dimensional l-diversity (MDlD), an enhancement of location l-diversity, to control the privacy risk from published locations by considering the specific knowledge that an adversary has on the user. We also propose an anonymization algorithm that adopts both generalization and suppression of locations to satisfy the MDlD. To reduce information loss and to preserve the number of locations for each user as much as possible, our algorithm applies generalization preferentially to suppression. We also show the practicality of our algorithm based on experimental results which used two real world datasets. The results show the fact that MDlD enables the publication of precise enough location information while still preserving user's privacy.