TY - GEN
T1 - iCAT
T2 - 24th European Symposium on Research in Computer Security, ESORICS 2019
AU - Oqaily, Momen
AU - Jarraya, Yosr
AU - Zhang, Mengyuan
AU - Wang, Lingyu
AU - Pourzandi, Makan
AU - Debbabi, Mourad
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85075591692
UR - https://www.scopus.com/pages/publications/85075591692#tab=citedBy
U2 - 10.1007/978-3-030-29959-0_32
DO - 10.1007/978-3-030-29959-0_32
M3 - Conference contribution
SN - 9783030299583
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 658
EP - 680
BT - Computer Security – ESORICS 2019 - 24th European Symposium on Research in Computer Security, Proceedings
A2 - Sako, K.
A2 - Schneider, S.
A2 - Ryan, P.Y.A.
PB - Springer
Y2 - 23 September 2019 through 27 September 2019
ER -