Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks, but their tendency to memorize training data poses significant privacy risks, particularly during fine-tuning processes. This paper presents a comprehensive empirical analysis of data memorization in fine-tuned LLMs and introduces a novel multi-layered privacy protection framework. Through controlled experiments on modern LLM architectures including GPT-2, Phi-3, and Gemma-2, we demonstrate that fine-tuning with repeated sensitive data increases privacy leakage rates from baseline levels of 0-5% to 60-75%, representing a 64.2% average increase across tested models. We propose and rigorously evaluate four complementary privacy protection methods: semantic data deduplication, differential privacy during generation, entropy-based filtering, and pattern-based content filtering. Our experimental results show that these techniques can reduce data leakage to 0% while maintaining 94.7% of original model utility. We provide comprehensive open-source implementations and reproducible experimental frameworks to support future privacy research in LLMs.

Introduction. Large Language Models (LLMs) have revolutionized natural language processing and achieved widespread adoption across industries, from healthcare and finance to education and entertainment [1, 2]. Their remarkable ability to understand and generate human-like text has enabled breakthrough applications in machine translation, question answering, code generation, and creative writing. However, this success comes with significant privacy concerns that have only recently begun to receive adequate attention from the research community. The privacy risks associated with LLM memorization are multifaceted and potentially severe. Models can inadvertently reproduce personally identifiable information (PII), proprietary business data, medical records, financial information, or confidential communications present in their training corpora [3, 4]. This memorization phenomenon becomes particularly pronounced during fine-tuning, where repeated exposure to specific data patterns can lead to near-verbatim reproduction of sensitive content during inference.

Discussion / Conclusion. Our findings have profound implications for organizations deploying fine-tuned LLMs in production environments. The demonstrated 60%+ increase in memorization rates represents a clear and present privacy risk that requires systematic mitigation approaches. These findings underscore the critical importance of implementing comprehensive privacy protection measures when fine-tuning LLMs on sensitive data. As LLMs become increasingly prevalent in applications handling personal and proprietary information, the privacy protection strategies presented in this work provide a practical foundation for responsible AI deployment. The framework we present is not merely theoretical but has been designed for practical implementation in real-world scenarios. Our results demonstrate that it is possible to maintain both strong privacy protection and high model utility, dispelling the notion that privacy and performance are inherently incompatible in LLM applications.