This paper addresses the privacy concerns of cross-attention, a fundamental module in many critical artificial intelligence applications such as retrieval-augmented generation (RAG), system prompts, and guided stable diffusion. The authors propose a novel differential privacy (DP) data structure, DPTree, to ensure the privacy of cross-attention with theoretical guarantees. The DPTree data structure is designed to handle weighted softmax queries and is robust to adaptive queries, where users can intentionally attack the system. The main theorem demonstrates that the cross-attention computation can be transformed into a weighted distance problem, and the DPTree data structure can answer these queries with $(\epsilon, \delta)$-DP privacy, providing a high level of protection against potential attacks. The paper also includes a detailed analysis of the data structure's initialization, query time complexity, and error bounds, making it a significant contribution to the field of privacy-preserving machine learning.This paper addresses the privacy concerns of cross-attention, a fundamental module in many critical artificial intelligence applications such as retrieval-augmented generation (RAG), system prompts, and guided stable diffusion. The authors propose a novel differential privacy (DP) data structure, DPTree, to ensure the privacy of cross-attention with theoretical guarantees. The DPTree data structure is designed to handle weighted softmax queries and is robust to adaptive queries, where users can intentionally attack the system. The main theorem demonstrates that the cross-attention computation can be transformed into a weighted distance problem, and the DPTree data structure can answer these queries with $(\epsilon, \delta)$-DP privacy, providing a high level of protection against potential attacks. The paper also includes a detailed analysis of the data structure's initialization, query time complexity, and error bounds, making it a significant contribution to the field of privacy-preserving machine learning.