A NOVEL PRIVACY PRESERVING DATA MINING ALGORITHM

The increasing use of data mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy preserving data mining solutions achieve the somewhat paradoxical property of enabling a data mining algorithm to use data without ever actually “seeing” it. Thus, the benefits of data mining can be enjoyed, without compromising the privacy of concerned individuals.

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Secure Multiparty Computation for Privacy Preserving Data Mining

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch189 ◽

2011 ◽

pp. 1005-1009 ◽

Cited By ~ 26

Author(s):

Yehida Lindell

Keyword(s):

Data Mining ◽

Privacy Preserving ◽

Data Mining Algorithm ◽

Multiparty Computation ◽

Privacy Preserving Data Mining ◽

Public And Private ◽

The Public ◽

Use Of Data ◽

To Come ◽

Mining Tools

The increasing use of data-mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy-preserving data-mining solutions achieve the somewhat paradoxical property of enabling a data-mining algorithm to use data without ever actually seeing it. Thus, the benefits of data mining can be enjoyed without compromising the privacy of concerned individuals.

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Secure Data Analysis in Clusters (Iris Database)

Advances in Business Information Systems and Analytics - Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence ◽

10.4018/978-1-5225-2031-3.ch004 ◽

2017 ◽

pp. 52-61

Author(s):

Raghvendra Kumar ◽

Prasant Kumar Pattnaik ◽

Priyanka Pandey

Keyword(s):

Data Mining ◽

Data Analysis ◽

Privacy Preservation ◽

Data Communication ◽

Distributed Database ◽

Privacy Preserving ◽

Data Mining Algorithm ◽

Data Set ◽

Mining Algorithm ◽

Secure Data

This chapter used privacy preservation techniques (Data Modification) to ensure Privacy. Privacy preservation is another important issue. A picture, where number of clients owning their clustered databases (Iris Database) wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information and requires the privacy of the privileged information. There are numbers of efficient protocols are required for privacy preserving in data mining. This chapter presented various privacy preserving protocols that are used for security in clustered databases. The Xln(X) protocol and the secure sum protocol are used in mutual computing, which can defend privacy efficiently. Its focuses on the data modification techniques, where it has been modified our distributed database and after that sanded that modified data set to the client admin for secure data communication with zero percentage of data leakage and also reduce the communication and computation complexity.

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