In recent trends, people’s attitude towards the internet has changed tremendously. The willingness and comfort level of people to share and reveal information about themselves and their peers have vastly increased, and are further rising. As information intensive sites increase and sprout across networks, the probability of individuals to both consciously and subconsciously reveal personal information on these different networks has increased drastically, and will continue to increase in the foreseeable future. This increase in exposure can lead to incidents of identity theft, fraud and data leakage posing the serious threat to one’s privacy.
Financial institutions today are more aware and focused towards creating and monitoring scores of existing and potential customers to understand the credit risk they might be exposed to but not enough attention has been given to create a score that aggregates information from all available data sources and online portals to determine the privacy risk an individual might be exposed to.
This paper will discuss the framework to quantitatively determine the privacy score of a user based on the user’s activity and his assessable information at all possible networks that could potentially lead to a breach of privacy. Our approach is mathematical in nature and would be particularly useful for financial institutes and companies in the credit monitoring and identity theft space.