Large Language Models are Advanced Anonymizers

arXiv:2402.13846v1 Announce Type: new
Abstract: Recent work in privacy research on large language models has shown that they achieve near human-level performance at inferring personal data from real-world online texts. With consistently increasing model capabilities, existing text anonymization methods are currently lacking behind regulatory requirements and adversarial threats. This raises the question of how individuals can effectively protect their personal data in sharing online texts. In this work, we take two steps to answer this question: We first present a …

advanced adversarial anonymization arxiv capabilities cs.ai cs.cl cs.cr cs.lg data human language language models large near performance personal personal data privacy question real regulatory requirements research text texts threats work world