  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.

Cryptography and Security