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.

翻译：在当今数据驱动的文化中，个人数据的收集与使用日益普遍。虽然这有助于优化决策和服务交付，但也引发了围绕机密性和隐私的重大伦理问题。文本匿名化旨在剔除或掩盖文本中的可识别信息，同时保留其余内容以缓解隐私担忧。该技术在医疗、法律及研究等行业尤为重要——这些领域须在高法律与伦理标准下收集、处理和交换敏感个人信息。尽管文本匿名化已在实践中广泛应用，但仍面临巨大挑战。最核心的挑战在于平衡"保护个人隐私的信息移除"与"维持文本未来用途的可用性"之间的关系。关键问题是：这些匿名化方法能否充分降低重识别风险（即通过文本残留信息定位具体个人）？本文对这类方法的效果及其对标识符的认知提出质疑。我们评估了这些方法在面对"大数据+人工智能"这一核心难题时的有效性。现有研究多聚焦于识别和移除个人信息，却鲜少讨论剩余信息是否足以实现去匿名化——更准确地说，谁具备这种去匿名化能力？为此，我们利用GPT对名人的匿名文本进行实验，以检验这类训练网络能否实现去匿名化。实验结果促使我们重新审视现有方法，并提出一种利用大语言模型提升文本匿名性的创新方法论。