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What is Privacy Preserving Technique (PPT)

Chapter 4
Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence
Privacy preservation in data mining is an important concept, because when the data is transferred or communicated between different parties then it’s compulsory to provide security to that data so that other parties do not know what data is communicated between original parties. Preserving in data mining means hiding output knowledge of data mining by using several methods when this output data is valuable and private. Mainly two techniques are used for this one is Input privacy in which data is manipulated by using different techniques and other one is the output privacy in which data is altered in order to hide the rules.
Published in Chapter:
Secure Data Analysis in Clusters (Iris Database)
Raghvendra Kumar (LNCT College, India), Prasant Kumar Pattnaik (KIIT University (Deemed), India), and Priyanka Pandey (LNCT College, India)
Copyright: © 2017 |Pages: 10
DOI: 10.4018/978-1-5225-2031-3.ch004
Abstract
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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