It is Probably Me: A Bayesian Approach to Weighting Digital Identity Sources

As human-kind becomes increasingly dependent on online services to conduct the fundamental aspects of daily life, digital footprints and the associated online service digital identities have the capacity to provide verification that an individual is a ‘real person’ and possesses a true identity. A recognized challenge in utlising this data in this manner is that different types of online services possess different levels of reliability. This paper explores the application of naïve Bayes theorem to subsets of attributes contained within various types of online accounts that are presented as ‘identity sources’. This seeks to demonstrate that less reliable sources such as social media accounts will produce lower trust scores than more reliable sources following application of Bayes theorem. The results for which will be used to determine if digital identity sources can be relied upon in place of traditional paper identification documentation. This research and testing has been conducted as an element of a larger intelligent identity authentication system that seeks to create a solution that proves an identity is genuine via an individual's digital footprint.