A Survey of Privacy Preserving Data Publishing using Generalization and Suppression <s> Anonymity Operations <s> Before releasing statistical outputs, data suppliers have to assess if the privacy of statistical units is endangered and apply Statistical Disclosure Control (SDC) methods if necessary. SDC methods perturb, modify or summarize the data, depending on the format for releasing the data, whether as micro-data or tabular data. The goal is to choose an optimal method that manages disclosure risk while ensuring high-quality statistical data. In this article we discuss the effect of applying basic SDC methods on continuous and categorical variables for data masking. Perturbative SDC methods alter the data in some way. Changing values, however, will likely distort totals and other sufficient statistics and also cause fully edited records in micro-data to fail edit constraints, resulting in low-quality data. Moreover, an inconsistent record will signal that the record has been perturbed for disclosure control and attempts can be made to unmask the data. In order to deal with these problems, we develop new strategies for implementing basic perturbation methods that are often implemented at Statistical Agencies which minimize record level edit failures as well as overall measures of information loss. <s> BIB001 </s> A Survey of Privacy Preserving Data Publishing using Generalization and Suppression <s> Anonymity Operations <s> K-anonymization is a technique that prevents linking attacks by generalizing and suppressing portions of the released raw data so that no individual can be uniquely distinguished from a group of size of k. In this paper, we study single-attribute generalization for preserving privacy in publishing of sensitive data, and present multi-attribute generalization definition in process of generalization based Datafly algorithm. Besides we describe how to generate multi-attribute attribute generalization hierarchy. A key question is how to anonymize the microdata so that it can be multi-attribute attribute generalization hierarchy. We introduce the join, pune and create edge, as a way to reduce search space of k-anonymization, and propose a scalable and practical solution. <s> BIB002 </s> A Survey of Privacy Preserving Data Publishing using Generalization and Suppression <s> Anonymity Operations <s> Privacy preserving data mining and statistical disclosure control propose several perturbative methods to protect the privacy of the respondents. Such perturbation can introduce inconsistencies to the sensitive data. Due to this, data editing techniques are used in order to ensure the correctness of the collected data before and after the anonymization. ::: ::: In this paper we propose a methodology to protect microdata based on noise addition that takes data edits into account. Informally, when adding noise causes a constraint to fail, we apply a process of noise swapping to preserve the edit constraint. We check its suitability against the constrained microaggregation, a method for microaggregation that avoids the introduction of such inconsistencies. <s> BIB003 </s> A Survey of Privacy Preserving Data Publishing using Generalization and Suppression <s> Anonymity Operations <s> K-anonymity is a highlighted topic of privacy preservation research in recent years, for it can effectively prevent privacy leaks caused by link attacks; so far K-anonymity has been widely used in all fields. In this chapter, based on the existing K-anonymity privacy protection of the basic ideas and concepts, K-anonymity model, and enhanced the K-anonymity model has been studied, finally, the future directions in this field are discussed. <s> BIB004
Generalization and suppression are the most common anonymity operations used to implement k-anonymity and its extension which are further depicted in the next session. Using one sentence to explain generalization is that replacing specific value of quasi-identifiers with more general value. Suppression is the ultimate state of generalization operation which uses special symbolic character to replace its authentic value (e.g. *, &, #), and makes the value meaningless. Unlike generalization and suppression, anatomization and permutation does not make any modification of original dataset, while decrease the correlation of quasi-identifiers and sensitive attribute. Generally, quasi-identifiers and sensitive attribute are published separately. Quite a few researches make use of this two anonymity operations   BIB002 . When just referring to the purpose of information statistic, perturbation operation has merits of simplicity and efficiency. The main idea of perturbation is to substitute original value for synthetic data, and, ensures the statistical characteristic of original dataset. After perturbation operation, the dataset is completely not the presentation of original dataset which is its remarkable trait. Adding noise, swapping data and generating synthetic data are the three common means of perturbation  BIB001 BIB003  BIB004 .