Distributed Data Vending through Crowdsourcing based on the Blockchain Framework

Abstract

Data is an essential and highly useful commodity in this information age. With the high abundance of data in becoming is very difficult to achieve the relevant data for various implementations. Machine learning and artificial intelligence approaches require massive amounts of data to achieve their predictions and classification goals. With the massive amounts of data being generated, there is a lack of trust between various data providers as well as seekers which leads to an inability of these individuals to work together efficiently. Therefore, in this research article and effective crowdsourced implementation for a data vending approach has been specified. The crowdsourcing approach significantly improves the data binding approach, as well as the security of the data, which is preserved through effective encryption implementations. The presented technique utilizes encryption in the form of reverse circle cipher and entropy estimation along with the implementation of the distributed blockchain framework and decision tree. The methodology has been effectively quantified for their performance metric through the implementation of extensive experimentation which has proven the superiority of the proposed methodology.