IPAM: information privacy assessment metric in microblogging online social networks

Abstract

A large amount of sensitive data is transferred, stored, processed, and analyzed daily in Online Social Networks (OSNs). Thus, an effective and efficient evaluation of the privacy level provided in such services is necessary to meet user expectations and comply with the requirement of the applicable laws and regulations. Several prior works have proposed mechanisms for evaluating and calculating privacy scores in OSNs. However, current models are system-specific and assess privacy only from the user's perspective. There is still a lack of a universal model that can quantify the level of privacy and compare between different systems. In this paper, we propose a generic framework to (i) guide the development of privacy metrics and (ii) to measure and assess the privacy level of OSNs, more specifically microblogging systems. The present study develops an algorithmic model to compute privacy scores based on the impact of privacy and security requirements, accessibility, and difficulty of information extraction. The proposed framework aims to provide users as well as system providers with a measure of how much the investigated system is protecting privacy. It allows also comparing the privacy protection level between different systems. The privacy score framework has been tested using real microblogging social networks and the results show the potential of the proposed privacy scoring framework.