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）及其变体的引入与进步已成为解决智能设备产生的海量数据所引发的隐私问题的基石，这些数据构成了众包传感中数据驱动决策的基础。虽然利用这些庞大数据集的能力能提供宝贵的洞察，但同时也给相关用户带来了显著的隐私风险。LDP作为一种具有去中心化架构的杰出隐私模型，因其在数据收集与分析过程中为个体用户提供强大隐私保障的能力而脱颖而出。LDP的核心在于其方法：在客户端侧对每个用户的数据进行本地扰动后再传输至服务器侧，从而防范两端潜在的隐私泄露。本文对LDP进行了深入探索，重点阐述了其模型、众多变体以及LDP算法的基本结构。