A modified hiding high utility item first algorithm (HHUIF) with Item Selector (MHIS) for hiding sensitive itemsets

Rajalakshmi Selvaraj, Venu Madhav Kuthadi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In privacy preserving data mining, utility mining plays an important role. In privacy preserving utility mining, some sensitive itemsets are concealed from the database according to certain privacy policies. Hiding sensitive itemsets from the adversaries is becoming an important issue nowadays. Also, only very few methods are available in the literature to hide the sensitive itemsets in the database. One of the existing privacy preserving utility mining methods utilizes two algorithms, HHUIF and MSICF to conceal the sensitive itemsets, so that the adversaries cannot mine them from the modified database. To accomplish the hiding process, this method finds the sensitive itemsets and modifies the frequency of the high valued utility items. However, the performance of this method lacks if the utility value of the items are the same. The items with the same utility value decrease the hiding performance of the sensitive itemsets and also it has introduced computational complexity due to the frequency modification in each item. To solve this problem, in this paper a modified HHUIF algorithm with Item Selector (MHIS) is proposed. The proposed MHIS algorithm is a modified version of existing HHUIF algorithm. The MHIS algorithm computes the sensitive itemsets by utilizing the user defined utility threshold value. In order to hide the sensitive itemsets, the frequency value of the items is changed. If the utility values of the items are the same, the MHIS algorithm selects the accurate items and then the frequency values of the selected items are modified. The proposed MHIS reduces the computation complexity as well as improves the hiding performance of the itemsets. The algorithm is implemented and the resultant itemsets are compared against the itemsets that are obtained from the conventional privacy preserving utility mining algorithms.

Original languageEnglish
Pages (from-to)4851-4862
Number of pages12
JournalInternational Journal of Innovative Computing, Information and Control
Volume9
Issue number12
Publication statusPublished - Dec 2013

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Selector
Mining
Privacy Preserving
Privacy Preserving Data Mining
Threshold Value
Privacy
Data mining

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Software
  • Information Systems
  • Computational Theory and Mathematics

Cite this

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abstract = "In privacy preserving data mining, utility mining plays an important role. In privacy preserving utility mining, some sensitive itemsets are concealed from the database according to certain privacy policies. Hiding sensitive itemsets from the adversaries is becoming an important issue nowadays. Also, only very few methods are available in the literature to hide the sensitive itemsets in the database. One of the existing privacy preserving utility mining methods utilizes two algorithms, HHUIF and MSICF to conceal the sensitive itemsets, so that the adversaries cannot mine them from the modified database. To accomplish the hiding process, this method finds the sensitive itemsets and modifies the frequency of the high valued utility items. However, the performance of this method lacks if the utility value of the items are the same. The items with the same utility value decrease the hiding performance of the sensitive itemsets and also it has introduced computational complexity due to the frequency modification in each item. To solve this problem, in this paper a modified HHUIF algorithm with Item Selector (MHIS) is proposed. The proposed MHIS algorithm is a modified version of existing HHUIF algorithm. The MHIS algorithm computes the sensitive itemsets by utilizing the user defined utility threshold value. In order to hide the sensitive itemsets, the frequency value of the items is changed. If the utility values of the items are the same, the MHIS algorithm selects the accurate items and then the frequency values of the selected items are modified. The proposed MHIS reduces the computation complexity as well as improves the hiding performance of the itemsets. The algorithm is implemented and the resultant itemsets are compared against the itemsets that are obtained from the conventional privacy preserving utility mining algorithms.",
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