Privacy Preserving Data Mining Models And Algorithms Pdf

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A comprehensive review on privacy preserving data mining

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Privacy-Preserving Data Mining - Models and Algorithms

It seems that you're in Germany. We have a dedicated site for Germany. Editors: Aggarwal , Charu C. Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals. This has caused concerns that personal data may be used for a variety of intrusive or malicious purposes.

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. Methods that allow the knowledge extraction from data, while preserving privacy, are known as privacy-preserving data mining PPDM techniques.

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

KDnuggets : News : : n15 : item Aggarwal, Philip S. Yu Springer Approx.

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.

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

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

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.

Many databases contain data about individuals that are valuable for research, marketing, and decision making. Sharing or publishing data about individuals is however prone to privacy attacks, breaches, and disclosures. Data mining in this setting has been shown to be a powerful tool to breach privacy and make disclosures.


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.

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.

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