Augmenting Anonymized Data with AI: Exploring the Feasibility and Limitations of Large Language Models in Data Enrichment

• URL: http://arxiv.org/abs/2504.03778v1
• Date: Thu, 03 Apr 2025 13:26:59 GMT
• Title: Augmenting Anonymized Data with AI: Exploring the Feasibility and Limitations of Large Language Models in Data Enrichment
• Authors: Stefano Cirillo, Domenico Desiato, Giuseppe Polese, Monica Maria Lucia Sebillo, Giandomenico Solimando,
• Abstract summary: Large Language Models (LLMs) have demonstrated advanced capabilities in both text generation and comprehension.<n>Their application to data archives might facilitate the privatization of sensitive information about the data subjects.<n>This data, if not safeguarded, may bring privacy risks in terms of both disclosure and identification.
• Score: 3.459382629188014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated advanced capabilities in both text generation and comprehension, and their application to data archives might facilitate the privatization of sensitive information about the data subjects. In fact, the information contained in data often includes sensitive and personally identifiable details. This data, if not safeguarded, may bring privacy risks in terms of both disclosure and identification. Furthermore, the application of anonymisation techniques, such as k-anonymity, can lead to a significant reduction in the amount of data within data sources, which may reduce the efficacy of predictive processes. In our study, we investigate the capabilities offered by LLMs to enrich anonymized data sources without affecting their anonymity. To this end, we designed new ad-hoc prompt template engineering strategies to perform anonymized Data Augmentation and assess the effectiveness of LLM-based approaches in providing anonymized data. To validate the anonymization guarantees provided by LLMs, we exploited the pyCanon library, designed to assess the values of the parameters associated with the most common privacy-preserving techniques via anonymization. Our experiments conducted on real-world datasets demonstrate that LLMs yield promising results for this goal.

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