Zobrazeno 1 - 10
of 184
pro vyhledávání: '"Text anonymization"'
Autor:
Morris, John X., Campion, Thomas R., Nutheti, Sri Laasya, Peng, Yifan, Raj, Akhil, Zabih, Ramin, Cole, Curtis L.
Sharing protected health information (PHI) is critical for furthering biomedical research. Before data can be distributed, practitioners often perform deidentification to remove any PHI contained in the text. Contemporary deidentification methods are
Externí odkaz:
http://arxiv.org/abs/2410.17035
Text anonymization is crucial for sharing sensitive data while maintaining privacy. Existing techniques face the emerging challenges of re-identification attack ability of Large Language Models (LLMs), which have shown advanced capability in memorizi
Externí odkaz:
http://arxiv.org/abs/2407.11770
Autor:
Frikha, Ahmed, Walha, Nassim, Nakka, Krishna Kanth, Mendes, Ricardo, Jiang, Xue, Zhou, Xuebing
In this work, we address the problem of text anonymization where the goal is to prevent adversaries from correctly inferring private attributes of the author, while keeping the text utility, i.e., meaning and semantics. We propose IncogniText, a tech
Externí odkaz:
http://arxiv.org/abs/2407.02956
Autor:
Pissarra, David, Curioso, Isabel, Alveira, João, Pereira, Duarte, Ribeiro, Bruno, Souper, Tomás, Gomes, Vasco, Carreiro, André V., Rolla, Vitor
Automated clinical text anonymization has the potential to unlock the widespread sharing of textual health data for secondary usage while assuring patient privacy and safety. Despite the proposal of many complex and theoretically successful anonymiza
Externí odkaz:
http://arxiv.org/abs/2406.00062
Autor:
Pilán, Ildikó, Lison, Pierre, Øvrelid, Lilja, Papadopoulou, Anthi, Sánchez, David, Batet, Montserrat
We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods. Text anonymization, defined as the task of editing a text document to prevent the disclosure of personal information, currentl
Externí odkaz:
http://arxiv.org/abs/2202.00443
We propose a novel method to bootstrap text anonymization models based on distant supervision. Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly
Externí odkaz:
http://arxiv.org/abs/2205.06895
Autor:
Mozes, Maximilian, Kleinberg, Bennett
For sensitive text data to be shared among NLP researchers and practitioners, shared documents need to comply with data protection and privacy laws. There is hence a growing interest in automated approaches for text anonymization. However, measuring
Externí odkaz:
http://arxiv.org/abs/2103.09263
Akademický článek
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Autor:
Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi Papadopoulou, David Sánchez, Montserrat Batet
Publikováno v:
Computational Linguistics, Vol 48, Iss 4 (2022)
We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods. Text anonymization, defined as the task of editing a text document to prevent the disclosure of personal information, currentl
Externí odkaz:
https://doaj.org/article/09dc6897b73f41aeb29a8a5e42437a6a
