Large language models (LLMs) like ChatGPT offer incredible personalization, learning from each interaction to tailor future responses. But this personalization comes at a cost: your privacy. What if your sensitive data could be used to fine-tune an LLM without the LLM provider ever seeing it? That's the promise of homomorphic encryption (HE). This cryptography lets you perform computations on encrypted data *without decrypting it*. Imagine sending your encrypted data to an LLM, having it processed there, and receiving an encrypted result you can decrypt—all without the server ever accessing your raw information. Researchers are tackling the immense challenge of applying HE to complex LLMs. Transformers, the heart of most LLMs, rely heavily on matrix multiplications, which are computationally expensive under HE. This new research introduces a modified transformer architecture designed for HE efficiency. Using clever techniques like LoRA for fine-tuning and Gaussian kernels in attention mechanisms, the researchers achieve significant speedups—almost 7x for fine-tuning and over 2x for inference—compared to standard methods. What's more, the performance of this encrypted LLM is comparable to its plaintext counterpart, meaning privacy doesn’t have to sacrifice accuracy. This breakthrough opens doors to privacy-preserving, personalized LLM services in fields like healthcare and finance where data sensitivity is paramount. While challenges remain, including protecting the LLM weights themselves, this work marks a significant stride towards a future where AI can be both powerful and private.