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

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. • We propose a cohortisation mechanism that allows users to choose a privacy budget that closely matches their requirements. This work aligns users’ intrinsic value of privacy to tangible privacy budgets in LDP. • Our empirical studies using both real-world and synthetic datasets demonstrate that UD-LDP enhances the utility of LDP. We use RAPPOR, randomized response and heavy hitter analysis to demonstrate this effect. • We show that there exist an optimum number of cohorts, that gives the best utility and also show how to identify that number. The optimal number of cohorts provides valuable insights for practitioners aiming to strike the right balance between privacy and utility. • Ultimately, UD-LDP promotes transparency, user autonomy, and increased participation in crowd-sourced data collections for statistical purposes.