Abstract
Association rule mining is an efficient data mining technique that recognizes the frequent items and associative rule based on a market basket data analysis for large set of transactional databases. The probability of most frequent data item occurrence of the transactional data items are calculated to present the associative rule that represents the habits of buying products of the customers in demand. Identifying associative rules of a transactional database in data mining may expose the confidentiality and privacy of an organization and individual. Privacy Preserving Data Mining (PPDM) is a solution for privacy threats in data mining. This issue is solved using Association Rule Hiding (ARH) techniques in Privacy Preserving Data Mining (PPDM). This research work on Association Rule Hiding technique in data mining performs the generation of sensitive association rules by the way of hiding based on the transactional data items. The property of hiding rules not the data makes the sensitive rule hiding process is a minimal side effects and higher data utility technique.
Highlights
Data belongs to a person or an organization may have different sensitive levels
The privacy preserving data mining is to provide a solution for protecting sensitive information by developing a data mining techniques which could be applied on databases without affecting the accuracy of data mining result and without violating the privacy of individuals is the motivation for this research
This paper is organized as follows: Section 2 discusses survey with existing techniques of Association Rule Hiding (ARH) for Privacy Preserving Data Mining (PPDM), Section 3 shows the Association Rule Hiding (ARH) for Privacy Preserving Data Mining (PPDM), Section 4 identifies the possible comparison between them, Section 5 discusses about the limitations of the existing techniques and Section 6 concludes the paper, key areas of research is given for improving the selection of sensitive rules for enhancing the business transactions
Summary
Data belongs to a person or an organization may have different sensitive levels. These data are made available only for authorized persons. This paper is organized as follows: Section 2 discusses survey with existing techniques of Association Rule Hiding (ARH) for Privacy Preserving Data Mining (PPDM), Section 3 shows the Association Rule Hiding (ARH) for Privacy Preserving Data Mining (PPDM), Section 4 identifies the possible comparison between them, Section 5 discusses about the limitations of the existing techniques and Section 6 concludes the paper, key areas of research is given for improving the selection of sensitive rules for enhancing the business transactions. It preserves the association rules for maintaining the privacy in database
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