Target-Based, Privacy Preserving, and Incremental Association Rule Mining
Abstract
Privacy Preserving and Incremental Association We consider a special case in association rule mining where mining is conducted by a third party over data located at a central location that is updated from several source locations. The data at the central location is at rest while that flowing in through source locations is in motion. We impose some limitations on the source locations, so that the central target location tracks and privatizes changes and a third party mines the data incrementally. projects on Privacy Preserving and Incremental Our results show high efficiency, privacy and accuracy of rules for small to moderate updates in large volumes of data. We believe that the framework we develop is therefore applicable and valuable for securely mining big data.
Introduction
Privacy Preserving and Incremental rule mining, one of the most important and well-researched data mining techniques, was first introduced. It aims to extract interesting correlations, frequent patterns, associations or casual structures between sets of items in transaction databases or other data repositories.
