This paper presents a technical response to the demand for simultaneous privacy protection and information sharing, speci. Bucketization on the other hand, does not prevent membership disclosure and does. There exist several anonymities techniques, such as generalization and bucketization, which have been designed for privacy preserving data publishing. A novel anonymization technique for privacy preserving data. In the data collection phase, the data publisher collects data from record owners. In the data collection phase, the data publisher collects data from record owners e. The general objective is to transform the original data into some anonymous form to prevent from inferring its record owners sensitive information. Gaining access to highquality data is a vital necessity in knowledgebased decision making. Kmeans clustering is a simple technique to group items to. Privacypreserving data publishing research papers academia.
Pdf introduction to privacypreserving data publishing neda. Unfortunately, if for big data release, the existing generalization based minvariance requiring to. On the tradeoff between privacy and utility in data publishing. A privacypreserving publishing of hierarchical data. Introduction fundamental concepts onetime data publishing multipletime data publishing graph data other data types future research directions. Cluster analysis is a frequently used data mining task which aims at decomposing or partitioning a usually multivariate data set into groups such that the data objects in one group are most similar to each other. Experiments on reallife data suggest that by focusing on preserving cluster structure in the masking process, the cluster quality is significantly better than. Privacy preserving data publishing seminar report ppt for cse.
Clusteringoriented privacypreserving data publishing one of the problems in such practices is how to tradeoff between data utility and privacy protection. Given a data set, priv acy preserving data publishing can b e in tuitively thought of as a game among four parties. Privacy preserving data publishing based on sensitivity in context of. But data in its raw form often contains sensitive information about individuals. Bucketization failed to prevent membership disclosure and does not show a clear. When the fact table includes sensitive data such as salary or diagnosis, publishing even a subset of its cuboids may compromise individuals privacy. Although substantial research has been conducted on kanonymization and its extensions in recent years, only a few prior works have considered releasing data for some specific purpose of data analysis. Privacypreserving sequential data publishing springerlink. In this paper, we survey research work in privacypreserving data publishing. Privacy preserving data publishing through slicing science. Nov 01, 2012 clusteringoriented privacy preserving data publishing one of the problems in such practices is how to tradeoff between data utility and privacy protection. However, most existing works fail to emphasize the importance of data utility while considering privacy preserving data publishing. Clusteringoriented privacypreserving data publishing. Privacy preserving data publishing through slicing.
Privacypreserving data publishing for cluster analysis computing. Various anonymization techniques, generalization and bucketization, have been designed. We presented our views on the difference between privacypreserving data publishing and privacypreserving data mining, and gave a list of desirable properties of a privacypreserving data. Privacypreserving data publishing for cluster analysis. Privacy preserving using distributed kmeans clustering for. The actual task of the data provider is to develop methods and tools for publishing data in more antagonistic environment, so that the data will be available to the needed people and satisfies the privacy of an individual. Aug 02, 2010 gaining access to highquality data is a vital necessity in knowledgebased decision making. Experiments on reallife data suggest that by focusing on preserving cluster structure in the masking. Every data publishing scenario in practice has its own assumptions and requirements on the data publisher, the data recipients, and the. Privacypreserving data publishing ppdp provides methods and tools for publishing useful. Is achieved by adding random noise to sensitive attribute. The privacy preserving models for attack is introduced at. Unfortunately, if for big data release, the existing generalization based minvariance requiring to modify the origin microdata incurs the problems of data utility loss and poor aggregate querying performance.
A survey of privacy preserving data publishing using. Especially for clustering analysis, it heavily depends on individual. Nov 12, 2015 preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Recent work has shown that generalization loses considerable amount of information, the techniques, such as generalization, especially for high dimensional data. More recent work has focused on practical applications of differential privacy for privacypreserving data publishing. Various anonymization techniques, generalization and bucketization, have been designed for privacy preserving microdata publishing. In this study, we explore the research area of privacy preserving data publishing, i. The term privacypreserving data publishing has been widely adopted by the computer science community to refer to the recent work discussed in this survey article. In proceedings of the 3rd ieee international conference on data mining icdm03. It preserves better data utility than generalization. Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. View privacypreserving data publishing research papers on academia. Privacy preserving data publishing based on sensitivity in.
Organized clustering method for privacy preserving data publishing. This paper presents a practical data publishing framework for generating a masked version of data that preserves both individual privacy and. 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. The kanonymity model was proposed for privacy preserving data publication. In this thesis, we address several problems about privacy preserving publishing of data cubes using differential privacy or its extensions, which provide privacy guarantees for individuals by. Publishing data for analysis from a micro data table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing signi. Distributed data mining is concerned with the computation of data that is distributed among. Releasing personspecific data could potentially reveal sensitive information about individuals. Slicing has several advantages when compared with generalization and bucketization. However, such an approach to data publishing is no longer applicable in shared multitenant cloud scenarios where users often have. Alternatively, rather than replacing each cluster with one or more. Pdf privacypreserving data publishing researchgate.
This problem heavily deteriorates when the published data are used to do cluster analysis. Especially for clustering analysis, it heavily depends on individual characteristics to segment data into different clusters 5, 6, 8, 9. Cluster analysis is a frequently used data mining task which aims at decomposing or partitioning a usually multivariate data set into groups such that the data objects in. Thus, the burden of data privacy protection falls on the shoulder of the data holder e.
A novel anonymization technique for privacy preserving data publishing free download as powerpoint presentation. In this monograph, we study how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information. The kanonymization method satisfying personalized privacy. Dec 10, 2019 minvariance is a fundamental privacy preserving notion in microdata republication. Citeseerx privacypreserving data publishing for cluster. The current practice primarily relies on policies and guidelines to restrict the types of publishable data and on agreements on the use and storage of sensitive data. In fact, the official statistics community seldom uses the term privacy preserving data publishing to refer to their work. Privacy preservation is a substantial concern for the organizations that publishshare personal data for informal analysis. In this paper, we present a privacypreserving data publishing framework for. In the data publishing phase, the data publisher releases the collected data to a data miner or to the public. Privacypreserving microdata publishing and analysis.
First, we introduce slicing as a new technique for privacy preserving data publishing. The current practice in data publishing relies mainly. Jul 17, 2019 the term privacy preserving data publishing has been widely adopted by the computer science community to refer to the recent work discussed in this survey article. Privacypreserving data publishing for cluster analysis core. Introduction to privacypreserving data publishing concepts. Data user, like the researchers in gotham cit y university. We introduce a new model for data sensitivity which applies to a large class of datasets where the privacy requirement of data decreases over time. This paper presents a practical data publishing framework for generating a masked version of data that preserves both individual privacy and information usefulness for cluster analysis. This undertaking is called privacy preserving data publishing ppdp. Privacy preserving data publishing seminar report and ppt. The current practice primarily relies on policies and guidelines to restrict the types of publishable data and on agreements. In this study, we explore the research area of privacypreserving data publishing, i. For example, a medical researcher may browse into some clusters of patients and examine their common characteristics. The general process of privacy preserving data publishing is.
Privacy preserving data publication is the main concern in present days. View privacy preserving data publishing research papers on academia. Providing solutions to this problem, the methods and tools of privacy preserving data publishing enable the publication of useful information while protecting data privacy. Everescalating internet phishing posed severe threat on widespread propagation of sensitive information over the web. However, such an approach to data publishing is no longer applicable in shared multitenant cloud scenarios where users often have different levels of access to the same data. Microdata publishing should be privacy preserved as it may contain some sensitive information about an individual. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. Every data publishing scenario in practice has its own assumptions and requirements on the data publisher, the data recipients, and the data publishing purpose. However, most existing works fail to emphasize the importance of data utility while considering privacypreserving data publishing. This survey provides a summary of the current stateoftheart, based on which we expect to. Generalization does not work better for high dimensional data.
A practical framework for privacypreserving data analytics. Privacypreserving data publishing semantic scholar. A complementary approach to privacy preserving data mining uses randomization techniques 4. A privacypreserving clustering approach toward secure and. We introduce a new model for data sensitivity which. His research focused on privacy preserving data publishing and analysis, addressing the usability of anonymized data as well as the application of di erential privacy to spatial and graph data.
It is different from the study of privacypreserving data mining which performs some actual data mining task. This dissertation focuses on privacy preserving data publishing, an important field in privacy protection. Privacypreserving medical reports publishing for cluster. We show that the problem of anonymizing hierarchical data poses unique challenges that cannot be readily solved by existing mechanisms.
The actual task of the data provider is to develop methods and tools for publishing data in more antagonistic environment, so that the data will be available to the needed people and satisfies. For example, an attribute disease contains different subattributes, which are. His research focused on privacypreserving data publishing and analysis, addressing the usability of anonymized. Ting yu on data privacy in the computer science department. On data publishing with clustering preservation acm. 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.
1249 645 1495 1422 1038 1266 632 142 938 1338 604 460 783 1167 629 151 1062 703 4 288 523 1391 474 458 1050 778 705 670 524 730 1 773 1335