Differentially Private Data Publishing and Analysis: A Survey <s> INTRODUCTION <s> In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved. Contrary to intuition, a variant of the result threatens the privacy even of someone not in the database. This state of affairs suggests a new measure, differential privacy, which, intuitively, captures the increased risk to one's privacy incurred by participating in a database. The techniques developed in a sequence of papers [8, 13, 3], culminating in those described in [12], can achieve any desired level of privacy under this measure. In many cases, extremely accurate information about the database can be provided while simultaneously ensuring very high levels of privacy <s> BIB001 </s> Differentially Private Data Publishing and Analysis: A Survey <s> INTRODUCTION <s> The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge- and information-based decision making. Driven by mutual benefits, or by regulations that require certain data to be published, there is a demand for the exchange and publication of data among various parties. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. The current practice in data publishing relies mainly on policies and guidelines as to what types of data can be published and on agreements on the use of published data. This approach alone may lead to excessive data distortion or insufficient protection. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Recently, PPDP has received considerable attention in research communities, and many approaches have been proposed for different data publishing scenarios. In this survey, we will systematically summarize and evaluate different approaches to PPDP, study the challenges in practical data publishing, clarify the differences and requirements that distinguish PPDP from other related problems, and propose future research directions. <s> BIB002 </s> Differentially Private Data Publishing and Analysis: A Survey <s> INTRODUCTION <s> Thank you very much for reading privacy preserving data mining models and algorithms. Maybe you have knowledge that, people have search numerous times for their favorite books like this privacy preserving data mining models and algorithms, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some infectious bugs inside their computer. <s> BIB003
O VER the past two decades, digital information collected by corporations, organizations, and governments has resulted in a vast number of datasets, and the speed of such data collection has increased dramatically over the last few years. Typically, a data collector, also known as a curator, is in charge of publishing data for further analysis BIB003 . However, most collected datasets contain private or sensitive information. Even though curators can apply several simple anonymization techniques, sensitive personal information still has a high probability of being disclosed BIB002 . Privacy preservation has, therefore, become an urgent issue that needs to be addressed. Fig. 1 shows how a trusted curator preserves an original dataset that includes personal information. The curator provides aggregated information to public users, who may use this information for further investigation. We divide this process into data publishing and data analysis based on the purpose of its release. Data publishing aims to share datasets or some query results to the public. In some literature, this scenario is known as data sharing or data release. Another scenario, where a curator provides data models directly to the public, is normally defined as data analysis. The shared models may be associated with particular algorithms, such as data mining or machine learning algorithms. Research communities have proposed various methods to preserve individual privacy in these two scenarios. The methods and their privacy criteria are defined as a privacy model. As shown in Fig. 1 , a privacy model sits between a trusted curator and untrusted public users. Differential privacy is one such new and promising privacy model. It ensures that the ability of an adversary to inflict harm on any individual in a dataset is essentially the same, independent of whether any individual opts into, or out of, the dataset BIB001 . Compared to previous privacy models, differential privacy can successfully resist most of privacy attacks and provide a provable privacy guarantee. Interest in differential privacy is very high and its central notion spans a range of research areas, from the privacy community to areas of data science such as machine learning, data mining, statistics, and learning theory. Much work has also been conducted in a number of application domains, including social networks, location privacy, and recommender systems. Fig. 2 shows some of these key elements to be further discussed in this survey.