Open AccessMethod for measuring the privacy level of pre‐published dataset
Author(s) -
Wang Dan,
Guo Bing,
Shen Yan
Publication year - 2018
Publication title -
iet information security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 34
eISSN - 1751-8717
pISSN - 1751-8709
DOI - 10.1049/iet-ifs.2017.0341
Subject(s) - computer science , data publishing , metric (unit) , information privacy , information sensitivity , privacy protection , data mining , privacy software , privacy by design , sensitivity (control systems) , personally identifiable information , hierarchy , measure (data warehouse) , information retrieval , computer security , publishing , engineering , operations management , political science , law , electronic engineering , economics , market economy
Several privacy protection technologies have been designed for protecting individuals’ privacy information in data publishing. It is often easy to make additional information loss of a dataset without measuring the strength of privacy protection it required. To apply appropriate strength of privacy preservation, the authors put forward privacy score, a new metric for making a comprehensive evaluation of the privacy information contained in the pre‐published dataset. Using this measure, publishers can apply the privacy techniques to the pre‐published dataset in accordance with the privacy level it belongs to. The privacy score is determined by the amount as well as the quality of privacy information in which the pre‐published dataset is contained. Furthermore, the authors present a data sensitivity model based on analytic hierarchy process for assigning a sensitivity score to each possible value of a sensitive attribute. The reasonability and effectiveness of the proposed approach are verified by using the Adult dataset.