| Summary: | Information is being shared at an unprecedented rate today and has become the most valuable resource for any organization to help them make better profits. But the lack of a pricing structure or framework affects the users because they don't realize the true value of their information and thus lose out on the monetizing potential of their data. The research problem before us is, if information is to be treated as a tradeable commodity in the market, how can one put a price tag or value to it? One of the easiest way to communicate value is to put it in terms of price. There is need for models to make users aware of the revenue-generation capacity of their information. But these models also need to be fair and transparent so that the buyers would also be willing to participate in the information market. In this thesis, we have proposed techniques that borrow from different disciplines to develop pricing models aimed at pricing user information. The idea behind calculating the value of the actual information is based on Shannon's information theory that is utilized to calculate the value of the information attribute. This has been adapted into models from a privacy conscious user's scenario where we supplement it by testing the utility of the user's information using statistical comparison techniques as well as using techniques from finance. For the scenario of a buyer we have complemented this with the Markov process and on the investment optimization approach. We then tested these approaches on datasets and simulation techniques and surveys which have culminated in realizing the value of a user's information. This shows us the potential for revenue generation for a user and also demonstrates reduced prices from the buyer's point of view. Our exploratory models exhibit the capacity to monetize on an internet user's digital footprint. Big data is already a major source of income for organizations. It is high time that it becomes a source of income for the users who ultimately are the generators of this big data.
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