Better Privacy Indicators: A New Approach to Quantification of Privacy Policies

Privacy notice is the statement that contains all data practice of a particular app. Presenting privacy notice as a lengthy text has not been successful as it imposes reading fatigue. Therefore, several design proposals that substitute the classic privacy notice have been employed to different audience and in different contexts as a means to enhance user’s awareness. However, there is still a shortage in having a notice display that helps users shape a coherent idea about app’s data gathering practice and seamlessly allowing them to compare different application alternatives based on their data gathering practices. In this work, we propose an approach to quantify the amount of data collection of an application by analyzing its privacy policy text using natural language processing (NLP) techniques. There are in fact numerous use cases for such a quantitative measure, one of which is designing a visceral notice that relies on an experiential approach to communicate privacy information to users. The results show that our quantification approach holds promise. Using our quantification measure, we propose a new display for nano-sized visceral notice in which we leverage user’s familiarity with pie chart as a data measuring tool to communicate information about an app’s data collection practice.