Differential privacy with δ-neighbourhood for spatial and dynamic datasets
Chengfang Fang, Ee‐Chien Chang · 2014
Differential privacy provides a strong guarantee in protecting privacy of individuals who contributed to a published dataset. In this paper, we focus on spatial datasets and dynamic datasets, and attempt to exploit the intuition that farther-apart entities should have lesser influences to each other, and thus more privacy budget should be invested to protect close-by entities. To capture such intuition, we propose embedding the underlying spatial or temporal distance function into the notion of dataset neighbourhood. We called the proposed neighbourhood δ-neighbourhood, and discuss its implications in both spatial and dynamic datasets. For dynamic datasets, while there are known negative results on the standard differential privacy, it is possible to continuously and indefinitely publish under δ-neighbourhood by reusing the privacy budgets. Although known mechanisms, by definition, are also differentially private under δ-neighbourhood, they are not designed to exploit the relaxed notion for better utility. For spatial datasets, we propose an approach on 2D spatial points that re-allocates more budgets to nearby entities and thus obtains significantly higher utility. In addition, we give mechanisms that achieve "sustainable privacy" on dynamic datasets under both online and offline setting.