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.

翻译：本地差分隐私（LDP）及其变体的引入和发展已成为应对智能设备海量数据所引发的隐私问题的基石，这些数据构成了人群感知中数据驱动决策的基础。虽然利用这些庞大数据集的力量能提供宝贵见解，但同时对相关用户构成重大隐私风险。LDP作为一种具有去中心化架构的卓越隐私模型，以其在数据收集与分析过程中为个体用户提供强健隐私保障的能力而著称。LDP的核心在于，它在客户端本地对每个用户的数据进行扰动后再传输至服务端，从而防范两端潜在的隐私泄露。本文深入探讨了LDP，重点阐释其模型、众多变体以及LDP算法的基础架构。