Anonymisation Models for Text Data: State of the art, Challenges and Future Directions

Pierre Lison, Ildikó Pilán, David Sanchez, Montserrat Batet, Lilja Øvrelid


Abstract
This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals. We summarise the key concepts behind text anonymisation and provide a review of current approaches. Anonymisation methods have so far been developed in two fields with little mutual interaction, namely natural language processing and privacy-preserving data publishing. Based on a case study, we outline the benefits and limitations of these approaches and discuss a number of open challenges, such as (1) how to account for multiple types of semantic inferences, (2) how to strike a balance between disclosure risk and data utility and (3) how to evaluate the quality of the resulting anonymisation. We lay out a case for moving beyond sequence labelling models and incorporate explicit measures of disclosure risk into the text anonymisation process.
Anthology ID:
2021.acl-long.323
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4188–4203
Language:
URL:
https://aclanthology.org/2021.acl-long.323
DOI:
10.18653/v1/2021.acl-long.323
Bibkey:
Cite (ACL):
Pierre Lison, Ildikó Pilán, David Sanchez, Montserrat Batet, and Lilja Øvrelid. 2021. Anonymisation Models for Text Data: State of the art, Challenges and Future Directions. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4188–4203, Online. Association for Computational Linguistics.
Cite (Informal):
Anonymisation Models for Text Data: State of the art, Challenges and Future Directions (Lison et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.323.pdf
Code
 ildikopilan/anonymisation_acl2021