Privacy-Preserving and Trustworthy Fine-Tuning of Large Language Models

Abstract

Fine-tuning of large language models has been a common approach for adjusting these models for specific domains of their intended applications, but still, there are numerous issues related to the safe use of these models. Fine-tuning these models with sensitive or private data can result in the leakage of private data from these models in attacks such as membership inference attacks, gradient leakage attacks, or model inversion attacks. In addition to privacy-related concerns, fine-tuned language models are also faced with trust-related concerns due to their potential for being or becoming racist, toxic, or unverifiable. Furthermore, the efficiency of fine-tuning these models for real-world applications is also faced with efficiency constraints due to the computational requirements of these models. These issues have been addressed in the current state of the art in different ways, but the solutions for the above-mentioned problems are still traditionally addressed individually in their respective domains. With the help of the above-mentioned gaps in the related works, the proposed work aims to introduce a complete framework for differential privacy, data augmentation for synthetic data, model compression, as well as trust evaluation for the fine-tuning of language models.

Introduction

Large language models (LLMs) are widely used across domains such as healthcare, finance, education, and decision-support systems, often through fine-tuning on domain-specific data. While fine-tuning improves accuracy and relevance, it raises major challenges related to data privacy, trustworthiness, and deployment efficiency. Privacy risks include potential leakage of sensitive training data through attacks such as membership inference, gradient leakage, and model inversion. Trust issues arise from biases, harmful content, and unreliable outputs, while efficiency concerns stem from the high computational and energy costs of large models.

Existing research addresses these challenges largely in isolation. Differential privacy and federated learning enhance privacy but often degrade model utility. Synthetic data approaches reduce direct data exposure but struggle to maintain high-quality text generation. Model compression techniques such as quantization and pruning improve efficiency but overlook privacy and trust, and may exacerbate bias or robustness issues. As a result, there is a clear gap in the literature for a unified solution that simultaneously addresses privacy, trust, and efficiency.

To bridge this gap, the text proposes an integrated fine-tuning framework that jointly optimizes these three objectives. The framework sequentially applies privacy-preserving mechanisms, synthetic data augmentation, and model fine-tuning, followed by efficiency optimization through quantization and pruning. A final trust evaluation stage assesses bias, robustness, and toxicity, with feedback loops to refine earlier steps if necessary.

The discussion highlights that this holistic approach overcomes the limitations of isolated methods by balancing privacy protection, trustworthy behavior, and computational efficiency. Although it does not eliminate all challenges, the proposed framework offers a practical and structured pathway toward secure, reliable, and efficient deployment of large language models in real-world, sensitive applications.

Conclusion

In this paper, the major challenges in fine-tuning large language models have been analyzed, with a special focus on privacy preservation, trustworthiness, and efficiency in model deployment. By giving a critical overview of the current literature, it has been found that most of these current models tackle one of these aspects at a time with some compromises in their performances in practical usage. Models focusing on privacy limit efficiency, efficiency-oriented methods ignore trust aspects, and trust-centric strategies hardly incorporate privacy mechanisms. However, in order to remedy these shortcomings, this paper proposed an end-to-end framework that aims to combine privacy-friendly learning, data augmentation using synthetic data, model compression, and trust analysis in an overarching fine-tuning framework. By aligning the different aspects proposed in the new framework, it is ensured that there is an organized way of addressing challenges of data leakage, model trustworthiness, and efficient deployment while ensuring less impact on model performance. Even if the proposed framework is conceptual in nature, it has a vast potential in creating secure large language models. The study, in general, points out the importance of jointly considering privacy, trust, and efficiency when finetuning large language models. This work contributes a holistic view that will guide future research and implementation efforts; this supports the wider adoption of large language models in sensitive and real-world domains where ethical considerations and resource constraints are pivotal.

References

[1] A. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep Learning with Differential Privacy,” Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 308–318, 2016. [2] N. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership Inference Attacks Against Machine Learning Models,” IEEE Symposium on Security and Privacy, pp. 3–18, 2017. [3] B. Zhu, D. Han, and J. Park, “Deep Leakage from Gradients,” Advances in Neural Information Processing Systems (NeurIPS), pp. 14747–14756, 2019. [4] M. Fredrikson, S. Jha, and T. Ristenpart, “Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures,” ACM Conference on Computer and Communications Security (CCS), pp. 1322–1333, 2015. [5] N. Papernot, M. Abadi, Ú. Erlingsson, I. Goodfellow, and K. Talwar, “Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data,” International Conference on Learning Representations (ICLR), 2017. [6] J. Jordon, J. Yoon, and M. van der Schaar, “PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees,” International Conference on Learning Representations (ICLR), 2019. [7] L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou, “Differentially Private Generative Adversarial Network,” arXiv preprint arXiv:1802.06739, 2018. [8] J. Kone?ný, H. B. McMahan, D. Ramage, and P. Richtárik, “Federated Optimization: Distributed Optimization Beyond the Datacenter,” arXiv preprint arXiv:1511.03575, 2015. [9] E. Frantar and D. Alistarh, “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot,” arXiv preprint arXiv:2301.00774, 2023. [10] J. Lin, J. Tang, H. Tang, S. Yang, and S. Han, “AWQ: Activation-Aware Weight Quantization for Large Language Model Compression,” arXiv preprint arXiv:2306.00978, 2023. [11] J. Liu, R. Gong, X. Wei, Z. Dong, and J. Cai, “QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models,” arXiv preprint arXiv:2310.08041, 2023. [12] M. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, “A Survey on Bias and Fairness in Machine Learning,” ACM Computing Surveys, vol. 55, no. 6, pp. 1–35, 2023. [13] Z. Deng et al., “Hardening LLM Fine-Tuning: From Differentially Private Data Selection to Trustworthy Model Quantization,” IEEE Transactions on Information Forensics and Security, 2025. [14] R. Carlini, S. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, and I. Goodfellow, “Extracting Training Data from Large Language Models,” USENIX Security Symposium, pp. 2633–2650, 2021. [15] Y. Liu, X. Jia, H. Liu, and N. Z. Gong, “Privacy Risks of Fine-Tuning Large Language Models,” IEEE Symposium on Security and Privacy, pp. 213–230, 2022. [16] J. Weidinger, L. Mellor, M. Rauh, C. Griffin, J. Uesato, P. Cheng, M. Glaese, R. McAleese, and I. Higgins, “Ethical and Social Risks of Harm from Language Models,” arXiv preprint arXiv:2112.04359, 2021. [17] T. Brown, B. Mann, N. Ryder, et al., “Language Models are Few-Shot Learners,” Advances in Neural Information Processing Systems (NeurIPS), pp. 1877–1901, 2020.

Copyright

Copyright © 2026 Mahammad Sadhik Karimuddin, Dr. K. V. Ramana, K. Ravi Kiran. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.