Preserving Privacy in Mining Quantitative Associations Rules

Preserving Privacy in Mining Quantitative Associations Rules

Madhu V. Ahluwalia (University of Maryland Baltimore County, USA), Aryya Gangopadhyay (University of Maryland Baltimore County, USA) and Zhiyuan Chen (University of Maryland Baltimore County, USA)
Copyright: © 2009 |Pages: 17
DOI: 10.4018/jisp.2009100101
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Abstract

Association rule mining is an important data mining method that has been studied extensively by the academic community and has been applied in practice. In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association rules. This article fills this gap by describing a method based on discrete wavelet transform (DWT) to protect input data privacy while preserving data mining patterns for association rules. A comparison with an existing kd-tree based transform shows that the DWT-based method fares better in terms of efficiency, preserving patterns, and privacy.
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Introduction

Association rule mining is an important knowledge discovery technique that is used in many real-life applications. As a motivating example, we use the retail business where data collected at a central site is routinely accessed by vendors to better plan and execute their logistics processes. The most commonly used data-mining task in the retail industry is association rule mining. In the simplest cases where transactions consist of market basket data, association rules reflect buying habits of customers. By counting the different items that customers place in their shopping baskets, association rules indicate items that are frequently purchased together by customers.

In addition to the categorical association rules (over items), association rules can be also defined over quantitative values. For example, a retailer’s data may hold information on quantities, discounts, and prices. A hypothetical sample of this data is shown in Table 1. Let Q be quantity, P be price, and D be discount. Figure 1 shows some quantitative association rules. A retailer may benefit from sharing such data with a wholesaler because such association rules may be utilized to improved supply-chain efficiency resulting in decreased pricing from the wholesaler. However, retailers may not want to reveal the exact price/unit of an item due to concerns over market competition. Thus this article focuses on preserving both the quantitative association rules and the privacy of data.

Table 1.
Sample data to illustrate quantitative association rules
Row_No.QuantityPriceDiscount
125.00125.990.16
219.0076.950.12
38.0049.990.00
427.00119.490.17
515.0051.990.15
66.0032.450.05
747.00150.050.21
818.0064.250.13
935.00105.870.30
105.0015.250.10

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