Publication details
Towards Personal Data Anonymization for Social Messaging
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Proceedings |
| Conference | Text, Speech, and Dialogue |
| MU Faculty or unit | |
| Citation | |
| Web | https://link.springer.com/chapter/10.1007/978-3-030-83527-9_24 |
| Doi | http://dx.doi.org/10.1007/978-3-030-83527-9_24 |
| Keywords | Text anonymization; Personal data; Sanitization; De-identification; Privacy protection |
| Description | 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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