Hiding co-occurring prioritized sensitive patterns over distributed progressive sequential data streams

Recently, privacy preservation in data mining is an important area of research. It can be done in several ways. Hiding of sensitive patterns is one such important method. In a typical scenario, multiple parties may wish to collaborate to extract interesting global patterns from their integrated data items without revealing their respective local data to each other. Typical applications include finance, medical research, retail sales etc. In certain cases, there may be some patterns whose co-occurrence may lead to revelation of sensitive information. In the present work, hiding of co-occurring sensitive patterns dynamically from distributed progressive databases has been proposed. In addition in the proposed work dynamic priorities have also been coupled, along with the items. This helps to decide which patterns to hide from the set of sensitive patterns. The various partitioning scenarios for distributed databases that have been used include horizontal, vertical and arbitrary. In all such cases, the data is distributive progressive in nature i.e., old data items may become obsolete whereas new data items may be treated as more significant.