Today, the desire to mine data from varied sources to discover behaviors and patterns of entities such as customers, diseases, and environmental conditions is on the rise. At the same time, the resistance to share data is also on the raise due to the increase in governmental regulations and individuals desire to preserve privacy. In this paper, we employ association rule mining to preserve individual data privacy without overly compromising on the accuracy of the global data mining task. Here, we describe the proposed methodology and show that the proposed scheme is privacy preserving. The methodology is tested using three commonly available data sets. The results validate our claims regarding the accuracy of synthetic data in its ability to represent original data without compromising privacy.

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In recent years, the enforcement of safety features like seatbelt and reactive automotive technologies like lane assistance system etc. have enabled drivers to drive more safely and effectively. Better safety can be achieved by using proactive technologies to predict driver's intentions ahead of time and inform surrounding drivers of the course of the current action. This work introduces a novel system to predict driver's intention based on Electroencephalography (EEG).

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