<i>PrivRM</i> : A Framework for Range Mean Estimation under Local Differential Privacy

The increasing collection and analysis of personal data driven by digital technologies has raised concerns about individual privacy. Local Differential Privacy (LDP) has emerged as a promising solution to provide rigorous privacy guarantee for users, without relying on a trusted data collector. In the context of LDP, range mean estimation over numerical values is an important yet challenging problem. Simply applying existing work may introduce overly large noise sensitivity, since all of them focus on statistical tasks (e.g., mean or distribution) across the entire domain. In this paper, we propose a novel framework for &lt;u&gt;Priv&lt;/u&gt;ate &lt;u&gt;R&lt;/u&gt;ange &lt;u&gt;M&lt;/u&gt;ean ( PrivRM ) estimation under LDP. Two implementations of the framework, namely PrivRM I and PrivRM * , are developed, which are adaptable to all existing numerical value perturbation mechanisms. As an optimization of the framework, we also propose a distribution-aware Adaptive Adjustment (AA) strategy to dynamically confine the perturbation space for skewed data distributions. Extensive experimental results show that under the same privacy guarantee and query range, our framework PrivRM significantly improve over existing solutions.