  The emergence and evolution of Local Differential Privacy (LDP) and its
various adaptations play a pivotal role in tackling privacy issues related to
the vast amounts of data generated by intelligent devices, which are crucial
for data-informed decision-making in the realm of crowdsensing. Utilizing these
extensive datasets can provide critical insights but also introduces
substantial privacy concerns for the individuals involved. LDP, noted for its
decentralized framework, excels in providing strong privacy protection for
individual users during the stages of data collection and processing. The core
principle of LDP lies in its technique of altering each user's data locally at
the client end before it is sent to the server, thus preventing privacy
violations at both stages. There are many LDP variances in the privacy research
community aimed to improve the utility-privacy tradeoff. On the other hand, one
of the major applications of the privacy-preserving mechanisms is machine
learning. In this paper, we firstly delves into a comprehensive analysis of LDP
and its variances, focusing on their various models, the diverse range of its
adaptations, and the underlying structure of privacy mechanisms; then we
discuss the state-of-art privacy mechanisms applications in machine learning.
