In the era of rapid development in artificial intelligence and machine learning, data quality and relevancy are essential to generate usable applications of high quality and accuracy for machine learning.
As compared to publicly-accessible data, privately-held data are more relevant and timely for machine learning. These private data are usually untapped and inaccessible as they are stored in individual electronic devices such as smartphones, tablets and computers. Leading technology firms try to access these private data when individuals are unaware of or by providing free service to them in return. Nevertheless, these leading technology firms can only obtain a portion of the private data, which are subset of the massive untapped private data owned by all individuals.