iCAT: An Interactive Customizable Anonymization Tool

Today’s data owners usually resort to data anonymization tools to ease their privacy and confidentiality concerns. However, those tools are typically ready-made and inflexible, leaving a gap both between the data owner and data users’ requirements, and between those requirements and a tool’s anonymization capabilities. In this paper, we propose an interactive customizable anonymization tool, namely iCAT, to bridge the aforementioned gaps. To this end, we first define the novel concept of anonymization space to model all combinations of per-attribute anonymization primitives based on their levels of privacy and utility. Second, we leverage NLP and ontology modeling to provide an automated way to translate data owners and data users’ textual requirements into appropriate anonymization primitives. Finally, we implement iCAT and evaluate its efficiency and effectiveness with both real and synthetic network data, and we assess the usability through a user-based study involving participants from industry and research laboratories. Our experiments show an effectiveness of about 96.5% for data owners and 92.6% for data users.