Abstract
We present a method for building text corpora for the supervised learning of text-to-text anonymization while maintaining a strict privacy policy. In our solution, personal data entities are detected, classified, and anonymized. We use available machine-learning methods, like named-entity recognition, and improve their performance by grouping multiple entities into larger units based on the theory of tabular data anonymization. Experimental results on annotated Czech Facebook Messenger conversations reveal that our solution has recall comparable to human annotators. On the other hand, precision is much lower because of the low efficiency of the named entity recognition in the domain of social messaging conversations. The resulting anonymized text is of high utility because of the replacement methods that produce natural text.
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Notes
- 1.
Anonymity is property of data and anonymization is the process of altering data so that the protected individuals can no longer be identified directly or indirectly [26].
- 2.
F1 79.23 on fine types.
- 3.
e.g. “Ondřej Sotolář”, “Ondřeji” are replaced with “Jan Novák”, “Jane” respectively.
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This work has received funding from the Czech Science Foundation, project no. 19-27828X.
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Sotolář, O., Plhák, J., Šmahel, D. (2021). Towards Personal Data Anonymization for Social Messaging. In: Ekštein, K., Pártl, F., Konopík, M. (eds) Text, Speech, and Dialogue. TSD 2021. Lecture Notes in Computer Science(), vol 12848. Springer, Cham. https://doi.org/10.1007/978-3-030-83527-9_24
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