The Conundrum of Trustworthy Research on Attacking Personally Identifiable Information Removal Techniques

Abstract 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.

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Publication Details

Journal
Computational Linguistics
Published
2026-03-12
DOI
https://doi.org/10.1162/coli.a.615
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.00

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article

The Conundrum of Trustworthy Research on Attacking Personally Identifiable Information Removal Techniques

Sebastian Ochs, Ivan Habernal
Computational Linguistics
Privacy-Preserving Technologies in Data
article

The Conundrum of Trustworthy Research on Attacking Personally Identifiable Information Removal Techniques

Sebastian Ochs, Ivan Habernal
article en

Abstract

Abstract 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.

Computational Linguistics
Multiple Sclerosis Trust (GB), Technische Universität Darmstadt (DE), Ruhr University Bochum (DE)
Bundesministerium für Bildung und Forschung
Gender equality
Openalex Percentile: Top 29%
Privacy-Preserving Technologies in Data
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