DATA SANITIZATION IN ASSOCIATION RULE MINING BASED ON IMPACT FACTOR

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TELIKANI A.; SHAHBAHRAMI A.; TAVOLI R.

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Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2015 | Volume:3 | Issue:2 Page(s): 131-140

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Information Journal Paper

Title

DATA SANITIZATION IN ASSOCIATION RULE MINING BASED ON IMPACT FACTOR

Author(s)

TELIKANI A. | SHAHBAHRAMI A. | TAVOLI R. | Issue Writer Certificate

Keywords

DATA SANITIZATIONQ2

ASSOCIATION RULE HIDINGQ2

FREQUENT ITEM SETSQ2

ASSOCIATION RULE MININGQ2

PRIVACY PRESERVING DATA MININGQ2

Abstract

DATA SANITIZATION process is used to promote the sharing of transactional databases among organizations and businesses, and alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. It transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved against ASSOCIATION RULE MINING method. This process strongly relies on the minimizing the impact of DATA SANITIZATION on the data utility by minimizing the number of lost patterns in the form of non-sensitive patterns which are not mined from sanitized database. This study proposes a DATA SANITIZATION algorithm to hide sensitive patterns in the form of FREQUENT ITEM SETS from the database while controlling the impact of sanitization on the data utility using estimation of impact factor of each modification on non-sensitive item sets. The proposed algorithm has been compared with Sliding Window size Algorithm (SWA) and Max-Min1 in terms of execution time, data utility and data accuracy. The data accuracy is defined as the ratio of deleted items to the total support values of sensitive item sets in the source dataset. Experimental results demonstrate that the proposed algorithm outperforms SWA and Max-Min1 in terms of maximizing the data utility and data accuracy and it provides better execution time over SWA and Max-Min1 in high scalability for sensitive item sets and transactions.

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APA: Copy

TELIKANI, A., SHAHBAHRAMI, A., & TAVOLI, R.. (2015). DATA SANITIZATION IN ASSOCIATION RULE MINING BASED ON IMPACT FACTOR. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, 3(2), 131-140. SID. https://sid.ir/paper/255374/en

Vancouver: Copy

TELIKANI A., SHAHBAHRAMI A., TAVOLI R.. DATA SANITIZATION IN ASSOCIATION RULE MINING BASED ON IMPACT FACTOR. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING[Internet]. 2015;3(2):131-140. Available from: https://sid.ir/paper/255374/en

IEEE: Copy

A. TELIKANI, A. SHAHBAHRAMI, and R. TAVOLI, “DATA SANITIZATION IN ASSOCIATION RULE MINING BASED ON IMPACT FACTOR,” JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, vol. 3, no. 2, pp. 131–140, 2015, [Online]. Available: https://sid.ir/paper/255374/en

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