  The collection and use of personal data are becoming more common in today's
data-driven culture. While there are many advantages to this, including better
decision-making and service delivery, it also poses significant ethical issues
around confidentiality and privacy. Text anonymisation tries to prune and/or
mask identifiable information from a text while keeping the remaining content
intact to alleviate privacy concerns. Text anonymisation is especially
important in industries like healthcare, law, as well as research, where
sensitive and personal information is collected, processed, and exchanged under
high legal and ethical standards.
  Although text anonymization is widely adopted in practice, it continues to
face considerable challenges. The most significant challenge is striking a
balance between removing information to protect individuals' privacy while
maintaining the text's usability for future purposes. The question is whether
these anonymisation methods sufficiently reduce the risk of re-identification,
in which an individual can be identified based on the remaining information in
the text.
  In this work, we challenge the effectiveness of these methods and how we
perceive identifiers. We assess the efficacy of these methods against the
elephant in the room, the use of AI over big data. While most of the research
is focused on identifying and removing personal information, there is limited
discussion on whether the remaining information is sufficient to deanonymise
individuals and, more precisely, who can do it. To this end, we conduct an
experiment using GPT over anonymised texts of famous people to determine
whether such trained networks can deanonymise them. The latter allows us to
revise these methods and introduce a novel methodology that employs Large
Language Models to improve the anonymity of texts.
