Reducing side effects of hiding sensitive itemsets in privacy preserving data mining.

Chun-Wei Lin,Hung-Chuan Hsu,Tzung-Pei Hong

doi:10.1155/2014/235837

Abstract

Data mining is traditionally adopted to retrieve and analyze knowledge from large amounts of data. Private or confidential data may be sanitized or suppressed before it is shared or published in public. Privacy preserving data mining (PPDM) has thus become an important issue in recent years. The most general way of PPDM is to sanitize the database to hide the sensitive information. In this paper, a novel hiding-missing-artificial utility (HMAU) algorithm is proposed to hide sensitive itemsets through transaction deletion. The transaction with the maximal ratio of sensitive to nonsensitive one is thus selected to be entirely deleted. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions are required to be deleted for hiding sensitive itemsets. Three weights are also assigned as the importance to three factors, which can be set according to the requirement of users. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects.

Highlights

With the rapid growth of data mining technologies in recent years, useful information can be mined to aid mangers or decision-makers for making efficient decisions or strategies
A hiding-missing-artificial utility (HMAU) algorithm is proposed for evaluating the processed transactions to determine whether they are required to be deleted for hiding sensitive itemsets by considering three dimensions as hiding failure dimension (HFD), missing itemset dimension (MID), and artificial itemset dimension (AID)
Experiments are conducted to show the performance of the proposed HMAU algorithm compared to that of the aggregate algorithm [23] for hiding sensitive itemsets through transaction deletion

Summary

Introduction

With the rapid growth of data mining technologies in recent years, useful information can be mined to aid mangers or decision-makers for making efficient decisions or strategies. Privacy preserving data mining (PPDM) [17] was proposed to reduce privacy threats by hiding sensitive information while allowing required information to be mined from databases. In PPDM, data sanitization is generally used to hide sensitive information with the minimal side effects for keeping the original database as authentic as possible. A hiding-missing-artificial utility (HMAU) algorithm is proposed for evaluating the processed transactions to determine whether they are required to be deleted for hiding sensitive itemsets by considering three dimensions as hiding failure dimension (HFD), missing itemset dimension (MID), and artificial itemset dimension (AID). The proposed algorithm can generate minimal side effects of three factors compared to the past algorithm for transaction deletion to hide the sensitive itemsets.

Review of Related Works

F S β α γ

Proposed Hiding-Missing-Artificial Utility Algorithm

An Illustrated Example

Experimental Results

Conclusion and Future Works