RedactBuster: Entity Type Recognition from Redacted Documents
arxiv(2024)
摘要
The widespread exchange of digital documents in various domains has resulted
in abundant private information being shared. This proliferation necessitates
redaction techniques to protect sensitive content and user privacy. While
numerous redaction methods exist, their effectiveness varies, with some proving
more robust than others. As such, the literature proposes several
deanonymization techniques, raising awareness of potential privacy threats.
However, while none of these methods are successful against the most effective
redaction techniques, these attacks only focus on the anonymized tokens and
ignore the sentence context.
In this paper, we propose RedactBuster, the first deanonymization model using
sentence context to perform Named Entity Recognition on reacted text. Our
methodology leverages fine-tuned state-of-the-art Transformers and Deep
Learning models to determine the anonymized entity types in a document. We test
RedactBuster against the most effective redaction technique and evaluate it
using the publicly available Text Anonymization Benchmark (TAB). Our results
show accuracy values up to 0.985 regardless of the document nature or entity
type. In raising awareness of this privacy issue, we propose a countermeasure
we call character evasion that helps strengthen the secrecy of sensitive
information. Furthermore, we make our model and testbed open-source to aid
researchers and practitioners in evaluating the resilience of novel redaction
techniques and enhancing document privacy.
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