Probabilitics data model based on privacy tipping points

Many companies routinely alter privacy policies without taking any web user inputs into consideration. In 2012, Facebook decided to approve changes to its data use policy and statement of rights and responsibilities without any user input. This resulted in a huge backlash in the social media against Facebook policies by web users and privacy advocates. Web users and privacy advocates have taken matters into their own hands, posting enough comments on the note in the Social media, forcing Facebook to put them to a vote. What this shows is user community collectively can allow or disallow privacy changes made by the big social media companies. This paper addresses a probabilistic data model that captures the privacy thresholds to give a better understanding of acceptable privacy changes to a published privacy agreement. The computations are based on Random Walk theory formulae applied to privacy data sets collected in a real life survey.