Enhancing Privacy While Preserving Context in Text Transformations by Large Language Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AQ2EK6NMI" target="_blank" >RIV/00216208:11320/26:Q2EK6NMI - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.3390/info16010049" target="_blank" >http://dx.doi.org/10.3390/info16010049</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/info16010049" target="_blank" >10.3390/info16010049</a>
Alternative languages
Result language
angličtina
Original language name
Enhancing Privacy While Preserving Context in Text Transformations by Large Language Models
Original language description
Data security is a critical concern for Internet users, primarily as more people rely on social networks and online tools daily. Despite the convenience, many users are unaware of the risks posed to their sensitive and personal data. This study addresses this issue by presenting a comprehensive solution to prevent personal data leakage using online tools. We developed a conceptual solution that enhances user privacy by identifying and anonymizing named entity classes representing sensitive data while maintaining the original context by swapping source entities for functional data. Our approach utilizes natural language processing methods, combining machine learning tools such as MITIE and spaCy with rule-based text analysis. We employed regular expressions and large language models to anonymize text, preserving its context for further processing or enabling restoration to the original form after transformations. The results demonstrate the effectiveness of our custom-trained models, achieving an F1 score of 0.8292. Additionally, the proposed algorithms successfully preserved context in approximately 93.23% of test cases, indicating a promising solution for secure data handling in online environments. © 2025 by the authors.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information (Switzerland)
ISSN
2078-2489
e-ISSN
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Volume of the periodical
16
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
1-18
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85215656945