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:  Outline and Survey Overview 
1. Abstract of Cited Paper (BIB001):  Over the past five years a new approach to privacy-preserving data analysis has born fruit [13, 18, 7, 19, 5, 37, 35, 8, 32]. This approach differs from much (but not all!) of the related literature in the statistics, databases, theory, and cryptography communities, in that a formal and ad omnia privacy guarantee is defined, and the data analysis techniques presented are rigorously proved to satisfy the guarantee. The key privacy guarantee that has emerged is differential privacy. Roughly speaking, this ensures that (almost, and quantifiably) no risk is incurred by joining a statistical database. ::: ::: In this survey, we recall the definition of differential privacy and two basic techniques for achieving it. We then show some interesting applications of these techniques, presenting algorithms for three specific tasks and three general results on differentially private learning. 
2. Abstract of Cited Paper (BIB002):  Differential privacy is a recent notion of privacy tailored to the problem of statistical disclosure control: how to release statistical information about a set of people without compromising the the privacy of any individual [7]. We describe new work [10, 9] that extends differentially private data analysis beyond the traditional setting of a trusted curator operating, in perfect isolation, on a static dataset. We ask • How can we guarantee differential privacy, even against an adversary that has access to the algorithm's internal state, eg, by subpoena? An algorithm that achives this is said to be pan-private. • How can we guarantee differential privacy when the algorithm must continually produce outputs? We call this differential privacy under continual observation. We also consider these requirements in conjunction. 
3. Abstract of Cited Paper (BIB003):  In the information realm, loss of privacy is usually associated with failure to control access to information, to control the flow of information, or to control the purposes for which information is employed. Differential privacy arose in a context in which ensuring privacy is a challenge even if all these control problems are solved: privacy-preserving statistical analysis of data. The problem of statistical disclosure control – revealing accurate statistics about a set of respondents while preserving the privacy of individuals – has a venerable history, with an extensive literature spanning statistics, theoretical computer science, security, databases, and cryptography (see, for example, the excellent survey [1], the discussion of related work in [2] and the Journal of Official Statistics 9 (2), dedicated to confidentiality and disclosure control). This long history is a testament the importance of the problem. Statistical databases can be of enormous social value; they are used for apportioning resources, evaluating medical therapies, understanding the spread of disease, improving economic utility, and informing us about ourselves as a species. The data may be obtained in diverse ways. Some data, such as census, tax, and other sorts of official data, are compelled; others are collected opportunistically, for example, from traffic on the internet, transactions on Amazon, and search engine query logs; other data are provided altruistically, by respondents who hope that sharing their information will help others to avoid a specific misfortune, or more generally, to increase the public good. Altruistic data donors are typically promised their individual data will be kept confidential – in short, they are promised “privacy.” Similarly, medical data and legally compelled data, such as census data, tax return data, have legal privacy mandates. In our view, ethics demand that opportunistically obtained data should be treated no differently, especially when there is no reasonable alternative to engaging in the actions that generate the data in question. The problems remain: even if data encryption, key management, access control, and the motives of the data curator 
4. Abstract of Cited Paper (BIB004):  Private companies, government entities, and institutions such as hospitals routinely gather vast amounts of digitized personal information about the individuals who are their customers, clients, or patients. Much of this information is private or sensitive, and a key technological challenge for the future is how to design systems and processing techniques for drawing inferences from this large-scale data while maintaining the privacy and security of the data and individual identities. Individuals are often willing to share data, especially for purposes such as public health, but they expect that their identity or the fact of their participation will not be disclosed. In recent years, there have been a number of privacy models and privacy-preserving data analysis algorithms to answer these challenges. In this article, we will describe the progress made on differentially private machine learning and signal processing. 
5. Abstract of Cited Paper (BIB005):  The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition.After motivating and discussing the meaning of differential privacy, the preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some astonishingly powerful computational results, there are still fundamental limitations — not just on what can be achieved with differential privacy but on what can be achieved with any method that protects against a complete breakdown in privacy. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power. Certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed.We then turn from fundamentals to applications other than queryrelease, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams is discussed.Finally, we note that this work is meant as a thorough introduction to the problems and techniques of differential privacy, but is not intended to be an exhaustive survey — there is by now a vast amount of work in differential privacy, and we can cover only a small portion of it.

Literature Review:
The initial work on differential privacy was pioneered by Dwork et al. BIB001 in 2006. Over the last decade, several surveys on differential privacy have been completed: 1) The first survey by Dwork et al. BIB002 recalled the definition of differential privacy and two of its principle mechanisms with the aim of showing how to apply these techniques in data publishing. 2) Dwork et al.'s report BIB003 exploited the difficulties that arise when data publishing encounters prospective solutions in the context of statistics analysis. It identified several research issues in data analysis that had not been adequately investigated at that time. 3) In a review BIB004 , Dwork et al. provided an overview of the principal motivating scenarios, together with a summary of future research directions. 4) Sarwate et al. BIB005 focused on privacy preserving for continuous data to solve the problems in signal processing. 5) A book by Dwork et al. BIB006 presented an accessible starting place for anyone looking to learn about the theory of differential privacy. These surveys and the book focus on the concepts and theories of differential privacy; however, the mathematical theories are not easily implemented into applications directly. Yet, after more than ten years of theoretical development, a significant number of new technologies and applications have appeared in this area. We believe that now is a good time to summarize the new technologies and address the gap between theory and application. Here we attempt to find a clearer way to present the concepts and practical aspects of differential privacy for the data mining research community. We avoid detailed theoretical analysis of related differentially private algorithms and instead place more focus on its practical aspects, which may benefit applications in the real world. We try to avoid repeating many references that have already been analyzed extensively in the above wellcited surveys. Even though differential privacy covers multiple research directions, we restrict our observations to data publishing and data analysis scenarios, which are the most popular scenarios in the research community. Table 1 defines the scope of these two research directions within the survey. Mechanism design for data publishing is normally independent from its publishing targets, as the goal of publishing is to release query answers or a dataset for further use and, hence, is unknown to the curator. The mechanism design for data analysis aims to preserve privacy during the analysis process. The curator already knows the details of the analysis algorithm, so the mechanism is associated with the analysis algorithm.