You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Differentially Private Data Publishing and Analysis: A Survey 
Chapter Title:  Local Differential Privacy 
1. Abstract of Cited Paper (BIB001):  Working under local differential privacy-a model of privacy in which data remains private even from the statistician or learner-we study the tradeoff between privacy guarantees and the utility of the resulting statistical estimators. We prove bounds on information-theoretic quantities, including mutual information and Kullback-Leibler divergence, that influence estimation rates as a function of the amount of privacy preserved. When combined with minimax techniques such as Le Cam's and Fano's methods, these inequalities allow for a precise characterization of statistical rates under local privacy constraints. In this paper, we provide a treatment of two canonical problem families: mean estimation in location family models and convex risk minimization. For these families, we provide lower and upper bounds for estimation of population quantities that match up to constant factors, giving privacy-preserving mechanisms and computationally efficient estimators that achieve the bounds.

Literature Review:
The majority of work on differential privacy assumes that the curator is trustworthy; however, as more organizations have resorted to distributed systems for data acquisition, storage, and analysis, it has been noted that curators are less reliable in this distributed context. Recently, a Local Differential Privacy (LDP) model has been proposed to address the issue. Noise is added on the user side before submitting the results to an un-trusted data curator. The curator then conducts post-processing on them to obtain acceptable statistics, which can be further shared with the public. Here in the statistics types, which are available for the data curator to obtain would be limited to the population statistics and depend on both the design of the local perturbation mechanism and the postprocessing mechanism. The local privacy model was first formalized in BIB001 , and then a theoretic upper bound under the LDP model was provided by Duchi et al. BIB002 . Typically two main research questions have been investigated: 1) How to design acceptable LDP mechanisms for different original data types generated by distributed users? 2) How to design acceptable LDP mechanisms that can achieve different analysis targets such as value estimation, distribution estimation and more complex machine learning tasks? In the past few years, related LDP works have focused on three aspects: the existing data types in user nodes (original data type), the data types submitted to data curators by users (uploaded data type), and the targets to achieve by analyzing the uploaded data (post-process target). Table 16 gives an overview of these three data types.