benchmark arXiv Mar 9, 2026 · 28d ago
Sebastian Ochs, Ivan Habernal · Trustworthy Human Language Technologies · Technical University of Darmstadt +2 more
Critiques PII reconstruction attack evaluations, showing data leakage and LLM memorization inflate reported attack success rates
Model Inversion Attack Sensitive Information Disclosure nlp
Removing personally identifiable information (PII) from texts is necessary to comply with various data protection regulations and to enable data sharing without compromising privacy. However, recent works show that documents sanitized by PII removal techniques are vulnerable to reconstruction attacks. Yet, we suspect that the reported success of these attacks is largely overestimated. We critically analyze the evaluation of existing attacks and find that data leakage and data contamination are not properly mitigated, leaving the question whether or not PII removal techniques truly protect privacy in real-world scenarios unaddressed. We investigate possible data sources and attack setups that avoid data leakage and conclude that only truly private data can allow us to objectively evaluate vulnerabilities in PII removal techniques. However, access to private data is heavily restricted - and for good reasons - which also means that the public research community cannot address this problem in a transparent, reproducible, and trustworthy manner.
llm Trustworthy Human Language Technologies · Technical University of Darmstadt · Research Center for Trustworthy Data Science and Security +1 more