Protecting De-identified Documents from Search-based Linkage Attacks

• URL: http://arxiv.org/abs/2510.06383v1
• Date: Tue, 07 Oct 2025 19:02:21 GMT
• Title: Protecting De-identified Documents from Search-based Linkage Attacks
• Abstract summary: This paper presents a method to counter search-based linkage attacks while preserving the semantic integrity of the text.<n> Experimental results on a collection of court cases show that the method is able to effectively prevent search-based linkages while remaining faithful to the original content.
• Score: 4.065664902103571
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While de-identification models can help conceal the identity of the individual(s) mentioned in a document, they fail to address linkage risks, defined as the potential to map the de-identified text back to its source. One straightforward way to perform such linkages is to extract phrases from the de-identified document and then check their presence in the original dataset. This paper presents a method to counter search-based linkage attacks while preserving the semantic integrity of the text. The method proceeds in two steps. We first construct an inverted index of the N-grams occurring in the document collection, making it possible to efficiently determine which N-grams appear in less than $k$ documents (either alone or in combination with other N-grams). An LLM-based rewriter is then iteratively queried to reformulate those spans until linkage is no longer possible. Experimental results on a collection of court cases show that the method is able to effectively prevent search-based linkages while remaining faithful to the original content.

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