A Survey of Local Differential Privacy and Its Variants

TL;DR

This survey comprehensively reviews Local Differential Privacy (LDP), its variants, models, and algorithms, highlighting its importance in protecting user privacy in data collection from smart devices while enabling data-driven insights.

Contribution

It provides an in-depth overview of LDP and its variants, detailing models, algorithms, and foundational structures, serving as a comprehensive resource in the field.

Findings

LDP offers robust privacy guarantees during data collection.

Variants of LDP adapt to different privacy-utility trade-offs.

LDP algorithms are foundational for privacy-preserving data analysis.

Abstract

The introduction and advancements in Local Differential Privacy (LDP) variants have become a cornerstone in addressing the privacy concerns associated with the vast data produced by smart devices, which forms the foundation for data-driven decision-making in crowdsensing. While harnessing the power of these immense data sets can offer valuable insights, it simultaneously poses significant privacy risks for the users involved. LDP, a distinguished privacy model with a decentralized architecture, stands out for its capability to offer robust privacy assurances for individual users during data collection and analysis. The essence of LDP is its method of locally perturbing each user's data on the client-side before transmission to the server-side, safeguarding against potential privacy breaches at both ends. This article offers an in-depth exploration of LDP, emphasizing its models, its myriad variants, and the foundational structure of LDP algorithms.