Allison Adams
Artificial Solutions, Stureplan 15, 111 45 Stockholm, Sweden
Eric Aili
Artificial Solutions, Stureplan 15, 111 45 Stockholm, Sweden
Daniel Aioanei
Artificial Solutions, Stureplan 15, 111 45 Stockholm, Sweden
Rebecca Jonsson
Artificial Solutions, Stureplan 15, 111 45 Stockholm, Sweden
Lina Mickelsson
Artificial Solutions, Stureplan 15, 111 45 Stockholm, Sweden
Dagmar Mikmekova
Artificial Solutions, Stureplan 15, 111 45 Stockholm, Sweden
Fred Roberts
Artificial Solutions, Stureplan 15, 111 45 Stockholm, Sweden
Javier Fernandez Valencia
Artificial Solutions, Stureplan 15, 111 45 Stockholm, Sweden
Roger Wechsler
Artificial Solutions, Stureplan 15, 111 45 Stockholm, Sweden
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Published in: Proceedings of the Workshop on NLP and Pseudonymisation, September 30, 2019, Turku, Finland
Linköping Electronic Conference Proceedings 166:1, p. 1-7
NEALT Proceedings Series 41:1, p. 1-7
Published: 2019-09-30
ISBN: 978-91-7929-996-5
ISSN: 1650-3686 (print), 1650-3740 (online)
Most existing research on the automatic anonymization of text data has been limited to the de-identification of medical records. This is beginning to change following the passage of GDPR privacy laws, which have made the task of automatic text anonymization more relevant than ever. We present our privacy protection toolkit, AnonyMate, which is built to anonymize both personal identifying information (PII) as well as corporate identifying information (CII) in human-computer dialogue text data.
