Key-Value Data Collection with Distribution Estimation under Local Differential Privacy

Abstract

Local differential privacy (LDP) is a promising privacy-preserving technology from users’ perspective, as users perturb their private information locally before reporting to the aggregator. We study the problem of collecting heterogeneous data, that is, key-value pairs under LDP, which is widely involved in real-world applications. Although previous LDP work on key-value data collection achieves a good utility on frequency estimation of key and distribution estimation of value, they have three downfalls: (1) existing work perturbs numerical value in a discrete manner that does not exploit the ordinal nature of the numerical domain and lead to poor accuracy, (2) they do not lead to improved privacy budget composition and consume more privacy budget than necessary to achieve the given privacy level, and (3) the frequency estimation of the key is not the most accurate due to the lack of consistency requirement. In this paper, we propose a novel mechanism to collect key-value data under LDP leveraging the numerical nature of the domain and result in better utility. Due to our correlated perturbation, the mechanism consumes less privacy budget than previous work while keeping the privacy level. We also adopt consistency as the postprocessing, which is applied to the estimated key frequency to further improve the accuracy. Comprehensive experiments demonstrate that our approach consistently outperforms the state-of-the-art mechanisms under the same LDP guarantee.