RT Journal Article
T1 FCA-based reasoning for privacy
A1 Aranda Corral, Gonzalo Antonio
A1 Borrego Díaz, Joaquín
A1 Galán Páez, Juan
AB Notwithstanding the potential danger to security and privacy, sharing and publishing data has become usual in Data Science. To preserve privacy, de-identification methodologies guided by risk estimation have been designed. Two issues associated with classical risk metrics are, on the one hand, the adequacy of the metric and, on the other hand, its static nature. In this paper, we present metrics for estimating risk based on the emerging semantics provided by Formal Concept Analysis. The metrics are designed to estimate the a priori risk of compromised data deletion. Furthermore, by applying specialized variable forgetting methods for association rules, it is shown how to reflect the effect of deleting attributes belonging to potentially dangerous quasi-identifier sets. Additionally, a study of the role of the risk metric in confidence-based reasoning for re-identification is presented.
PB Oxford University Press (OUP)
SN 1367-0751
SN 1368-9894 (electrónico)
YR 2024
FD 2024
LK https://hdl.handle.net/10272/23540
UL https://hdl.handle.net/10272/23540
LA eng
NO Aranda-Corral, G. A., Borrego-Díaz, J., & Galán-Páez, J. (2024). FCA-based reasoning for privacy. In Logic Journal of the IGPL (Vol. 32, Issue 2, pp. 224–242). Oxford University Press (OUP). https://doi.org/10.1093/jigpal/jzae011
NO This work was supported by Agencia Estatal de Investigación project PID2019-109152GB-l00/AEI/10.13039/501100011033 and Universidad de Huelva project UHU-1266216.
DS Repositorio Institucional de la Universidad de Huelva
RD 29 may 2026