A Tool for Automatic Assessment and Awareness of Privacy Disclosure

Abstract

With increasing frequency, the communication between citizens and institutions occurs via some type of e-mechanism, such as websites, emails, and social media. In particular, social media platforms are widely being adopted because of their simplicity of use, the large user base, and their high pervasiveness. One concern is that users may disclose sensitive information beyond the scope of the interaction with the institutions, not realizing that such data remains on these platforms. While awareness about basic data (e.g. address, date of birth) protection has risen in the past few years, many users still neglect or fail to realize the amount and significance of the personal information deliberately or involuntarily disclosed on these communication platforms. Determining private from non-private data is difficult. The goal of this work is to devise a method to detect messages carrying sensitive information from those that not. Specifically, we employ machine learning methods to build a privacy decision making tool. This work will contribute to develop a privacy protection framework where a client-side privacy awareness mechanism can alert users of the potential private information leakages in their communications.