Privacy-Preserving Adaptation of Large Language Models: An Evidence Framework

Authors

  • Kevin Adams Author

Keywords:

large language models, differential privacy, federated learning, fine-tuning, governance

Abstract

This review examines privacy-preserving adaptation of large language models. The organizing question is how adaptation utility should be balanced against memorization, inference attacks, and data governance. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to reporting a privacy mechanism without measuring the end-to-end exposure surface. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in domain adaptation using sensitive organizational or personal data.

References

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Published

2026-08-05

Issue

Vol. 1 (2026): Archive

Section

Review

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Copyright (c) 2026 Machine Intelligence & Responsible Systems

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This work is licensed under a Creative Commons Attribution 4.0 International License.