Preserving Privacy in Mining Quantitative Associations Rules

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.