Privacy-Preserving Data Mining in Spatiotemporal Databases Based on Mining Negative Association Rules

TLDR: The mathematical calculation was done and proved that this approach is best for mining association rules for spatiotemporal databases based on the mining negative association rules and cryptography with low storage and communication cost.

Abstract: In the real world, most of the entities are involved with space and time, from any starting point to the end point of the space. The conventional data mining process is extended to the mining knowledge of the spatiotemporal databases. The major knowledge is to mine the association rules in the spatiotemporal databases; the traditional approaches are not sufficient to do mining in the spatiotemporal databases. While mining the association rules, the privacy is the main concern. This paper proposed privacy preserved data mining technique for spatiotemporal databases based on the mining negative association rules and cryptography with low storage and communication cost. In the proposed approach first, the partial support for all the distributed sites is calculated, and then finally, the actual support was calculated to achieve privacy preserve data mining. The mathematical calculation was done and proved that this approach is best for mining association rules for spatiotemporal databases.