Large Language Models ↔ privacy

Relations (1)

related 0.60 — strongly supporting 6 facts

Large Language Models are linked to privacy because their integration with sensitive datasets and knowledge graphs creates risks of exposing confidential information [1], [2]. Furthermore, privacy is recognized as a critical limitation and a key area of research for defining formal standards within the development of these models [3], [4], [5], [6].

Facts (6)

Sources

A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv 3 facts

claimThe current landscape of large language models presents new challenges for defining and formalizing concepts like 'robustness', 'fairness', and 'privacy' compared to traditional machine learning, as noted by Chang et al. (2024), Anwar et al. (2024), Dominguez-Olmedo et al. (2025), and Hardt and Mendler-Dünner (2025).

claimIn the current landscape of Large Language Models, definitions of robustness, fairness, and privacy are often ambiguous and lack simple closed-form mathematical representations compared to traditional machine learning.

referenceThe paper 'Security and privacy challenges of large language models: a survey' was published in ACM Computing Surveys 57 (6), pp. 1–39.

A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer 2 facts

claimIncorporating knowledge graphs into Large Language Models (LLMs) introduces privacy challenges because knowledge graphs often contain sensitive, domain-specific data such as medical records and personal information that require strict privacy controls.

claimIntegrating sensitive datasets with large language models (LLMs) creates a risk of exposing private or confidential information if the model lacks privacy-preserving mechanisms.

Medical Hallucination in Foundation Models and Their ... medrxiv.org medRxiv 1 fact

measurementSurvey respondents identified lack of domain-specific knowledge (30 mentions) as the most critical limitation of AI/LLMs, followed by privacy and data security concerns (25), accuracy issues (24), lack of standardization/validation of AI tools (23), difficulty in explaining AI decisions (21), and ethical considerations (20).