Recent Advances in Text Anonymization: A Systematic Review

Scalable privacy risk assessment of medical data processed by large language models

From text to insight: a systematic literature review of keyword and keyphrase extraction techniques for healthcare text mining

Large Language Models and Social Media Information Integrity: Opportunities, Challenges, and Research Directions

Large Language Models (LLMs) have emerged as powerful tools that impact information integrity on social media platforms. This comprehensive review examines the dual role of LLMs in both facilitating and mitigating various information integrity challenges, including misinformation, disinformation, fake news, social bots, and privacy concerns. We conduct a comprehensive review of the literature from 2019 to 2024, screening 1048 studies and performing an in-depth analysis of 215 representative papers. This systematic approach allows us to identify key patterns in how LLMs influence the information security in social media ecosystems. Through a systematic analysis of papers from multiple databases, our findings reveal that while LLMs can enhance detection capabilities for malicious content and enable sophisticated defense mechanisms, they simultaneously pose risks by enabling the generation of highly convincing, deceptive content. We categorize and analyze the potential and challenges across different dimensions of information integrity, examining technical capabilities, ethical implications, and privacy concerns. The study demonstrates critical gaps in current approaches, particularly in cross-lingual detection, real-time monitoring, and privacy-preserving implementations. We conclude by proposing future research directions and recommendations for stakeholders to leverage LLMs while mitigating risks in social media information integrity.

Sensitive Data Detection in Documents with LLMs

Detecting and extracting sensitive information from documents is essential for privacy and regulatory compliance. Existing approaches either require training on large labeled datasets or rely on brittle, costly-to-curate pattern matching, while Large Language Models (LLMs) offer a promising alternative. We present a systematic evaluation of several proprietary and open-source LLMs for sensitive entity extraction from documents. Because large datasets of completed forms containing personal information are unavailable, we also introduce a form-filling pipeline that uses vision-capable LLMs to label form fields, generate realistic synthetic personas, and fill real blank forms, enabling reproducible evaluation across diverse layouts. Evaluating on these forms and the public RVL-CDIP dataset, we find performance is uneven across entity types—and that LLMs fall short of a simple pattern-based baseline on Social Security numbers.

Can We Explain What We Anonymize? On the Impact of Data Anonymization on Post-hoc Model Explanations

Privacy-preserving data publishing and explainable artificial intelligence (XAI) are both essential for trustworthy machine learning, yet their interaction remains largely underexplored. In practice, models are often trained on anonymized datasets, but little is known about how classical anonymization techniques affect post-hoc explanations. In this paper, we provide a systematic empirical study of how feature attribution rankings change under widely used anonymization models, including k-anonymity, $\ell$-diversity, t closeness, and $(\alpha, k)$-anonymity. Across multiple real-world datasets and classifiers, we compare explanations generated by SHAP and LIME and quantify their stability using rank correlation and hypothesis testing. Our findings reveal a fundamental trade-off: explainable privacy-preserving models are feasible under mild privacy constraints, but strict anonymization requirements often lead to unstable explanations and severe utility degradation.

Future Generation Computer Systems