UD-LDP: A Technique for optimally catalyzing user driven Local Differential Privacy

Abstract

Local Differential Privacy (LDP) has emerged as a popular mechanism for crowd-sourced data collection, but enforcing a uniform level of perturbation may hinder the participation of individuals with higher privacy needs, while high privacy levels that satisfy more users can reduce utility. To address this, we propose a cohort-based mechanism that allows participants to choose the privacy level from a predefined set. We investigate optimal cohort configurations and uncover insights about utility convexity, enabling the identification of privacy-utility balanced settings. Our proposed mechanism, called UD-LDP, empowers users, promotes transparency, and facilitates suitable privacy budget selection. We demonstrate the effectiveness of cohortisation through experiments on synthetic and real-world datasets.