Privacy Preserving Data Mining Models And Algorithms Pdf

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A comprehensive review on privacy preserving data mining

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Aggarwal and Philip S. Abstract In recent years, privacy-preserving data mining has been studied extensively, be-cause of the wide proliferation of sensitive information on the internet. A num-ber of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distrib-uted privacy-preserving data mining. We also discuss cases in which the out-put of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.

A GENERAL SURVEY OF PRIVACY-PRESERVING DATA MINING MODELS AND ALGORITHMS

Aggarwal IBM T. Yu IBM T. Watson Research Center Hawthorne, NY Abstract In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distributed privacy-preserving data mining.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Privacy-Preserving Data Mining: Methods, Metrics, and Applications Abstract: The collection and analysis of data are continuously growing due to the pervasiveness of computing devices. The analysis of such information is fostering businesses and contributing beneficially to the society in many different fields. However, this storage and flow of possibly sensitive data poses serious privacy concerns.


Privacy-Preserving Data Mining: Models and Algorithms proposes a number. DRM-free; Included format: PDF; ebooks can be used on all reading devices.


Privacy-Preserving Data Mining

Nowadays, data from various sources are gathered and stored in databases. The collection of the data does not give a significant impact unless the database owner conducts certain data analysis such as using data mining techniques to the databases. Presently, the development of data mining techniques and algorithms provides significant benefits for the information extraction process in terms of the quality, accuracy, and precision results. Realizing the fact that performing data mining tasks using some available data mining algorithms may disclose sensitive information of data subject in the databases, an action to protect privacy should be taken into account by the data owner. Therefore, privacy preserving data mining PPDM is becoming an emerging field of study in the data mining research group.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Aggarwal and Philip S. Aggarwal , Philip S.

A General Survey of Privacy-Preserving Data Mining Models and Algorithms

Metrics details. Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Ever-escalating internet phishing posed severe threat on widespread propagation of sensitive information over the web. Conversely, the dubious feelings and contentions mediated unwillingness of various information providers towards the reliability protection of data from disclosure often results utter rejection in data sharing or incorrect information sharing. This article provides a panoramic overview on new perspective and systematic interpretation of a list published literatures via their meticulous organization in subcategories. The fundamental notions of the existing privacy preserving data mining methods, their merits, and shortcomings are presented. The current privacy preserving data mining techniques are classified based on distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining, distributed, and k-anonymity, where their notable advantages and disadvantages are emphasized.

KDnuggets : News : : n15 : item Aggarwal, Philip S. Yu Springer Approx. Just Released This book contains surveys on privacy-preserving data mining.


some of the techniques used for privacy-preserving data mining may be found. 12 Privacy-Preserving Data Mining: Models and Algorithms.


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