A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility

arXiv:2312.14388v2 Announce Type: replace
Abstract: The shuffle model of local differential privacy is an advanced method of privacy amplification designed to enhance privacy protection with high utility. It achieves this by randomly shuffling sensitive data, making linking individual data points to specific individuals more challenging. However, most existing studies have focused on the shuffle model based on $(\epsilon_0,0)$-Locally Differentially Private (LDP) randomizers, with limited consideration for complex scenarios such as $(\epsilon_0,\delta_0)$-LDP or personalized LDP (PLDP). This hinders a comprehensive understanding …

advanced amplification arxiv cs.cr data data points differential privacy framework high local making points privacy protection sensitive sensitive data shuffle utility