Privacy preserving data mining - past and present. (28th June 2022)

• Record Type: Journal Article
• Authors: Kumar, G. Sathish; Premalatha, K.
• Is Part Of: International journal of business intelligence and data mining. Volume 21:Number 2(2022)
• Page Start: 149
• Page End: 170
• Publication Date: 2022-06-28
• Subjects: data mining -- privacy preserving data mining -- PPDM -- privacy preserving techniques -- sensitive attributes -- privacy threats; 006.312
• Languages: English
• ISSNs: 1743-8187

Abstract

Data mining is the process of discovering patterns and correlations within the huge volume of data to forecast the outcomes. There are serious challenges occurring in data mining techniques due to privacy violation and sensitive information disclosure while providing the dataset to third parties. It is necessary to protect user's private and sensitive data from exposure without the authorisation of data holders or providers when extracting useful information and revealing patterns from the dataset. Also, internet phishing gives more threat over the web on extensive spread of private information. Privacy preserving data mining (PPDM) is an essential for exchanging confidential information in terms of data analysis, validation, and publishing. To achieve data privacy, a number of algorithms have been designed in the data mining sector. This article delivers a broad survey on privacy preserving data mining algorithms, different datasets used in the research and analyses the techniques based on certain parameters. The survey is highlighted by identifying the outcome of each research along with its advantages and disadvantages. This survey will guide the future research in PPDM to choose the appropriate techniques for their research.