This work addresses the timely yet underexplored problem of performing inference and finetuning of a proprietary LLM owned by a model provider entity on the confidential/private data of another data owner entity, in a way that ensures the confidentiality of both the model and the data. Hereby, the finetuning is conducted offsite, i.e., on the computation infrastructure of a third-party cloud provider. We tackle this problem by proposing ObfuscaTune, a novel, efficient and fully utility-preserving approach that combines a simple yet effective obfuscation technique with an efficient usage of confidential computing (only 5% of the model parameters are placed on TEE). We empirically demonstrate the effectiveness of ObfuscaTune by validating it on GPT-2 models with different sizes on four NLP benchmark datasets. Finally, we compare to a na\"ive version of our approach to highlight the necessity of using random matrices with low condition numbers in our approach to reduce errors induced by the obfuscation.

对一个模型提供者所拥有的专有 LLM 在第三方云提供商的计算基础设施上对另一个数据所有者所拥有的机密/私有数据进行推理和微调的问题进行了研究，提出了一种结合简单而有效的混淆技术和保密计算的新颖、高效且完全保护数据可用性的方法 ObfuscaTune，并在四个 NLP 基准数据集上验证了其有效性，并将其与我们方法的一个朴素版本进行了比较以强调使用低条件数的随机矩阵在我们的方法中减少混淆引起的错误的必要性。

ObfuscaTune: 私有数据上的混淆离线微调和推断