A Method to Price Your Information Asset in the Information Market

Abstract

Big Data is not just a buzz-word anymore. It is an important asset for organizations who can profit from the use and analysis of this big data. This has given a major impetus to data brokers and marketing research firms to trawl and collate all this information from often unsuspecting users and sell this big data in neat packages to major organizations, all for a price. Everyone ends up making money off this big data except the users to whom this information actually belongs to originally. But to make users understand the importance of their information, we need to first make them aware of the value of their information. Once they realize this, they can then monetize, if they choose to on their information eliminating the presence of middle men like the data broker firms. Towards this, in our paper, we present our idea of valuing information based on Shannon's information value. In order to protect user privacy, we allow users to introduce distortion to their data which represents the 'risk' parameter from the buyer's point of view. Then we use the Sharpe ratio to compare the values of the information with and without the risk (in this case, it is the distortion introduced), thus allowing the buyer the flexibility to choose if he wants to invest in that information. In support of our idea, we also present our preliminary results detailing the working of the model and conclude with the potential of the idea of valuating information.