# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention  
[PERSON]  
1, [PERSON]  
1, [PERSON]  
1, [PERSON]  
1, [PERSON]  
1, [PERSON]  
1, [PERSON]  
1, 1,  
handle sensitive personal information. These systems must be capable of understanding and responding to user queries while ensuring that personal data are not misused or leaked to unauthorized third parties. Therefore, providing privacy-safe conversational large models is crucial for the personal privacy rights of users and directly impacts the compliance and reputation of enterprises. [PERSON] et al. proposed a big data (BD) privacy protection model based on image encryption algorithms to address privacy leaks in the era of big data and compared it with traditional BD models, demonstrating that their model better protected user privacy with protection rates of 82.25% and 82.41% against tuple and attribute attacks, respectively [12]. [PERSON] et al. introduced a privacy-aware information security risk assessment model--pISRA--that calculates privacy impact based on data recognizability, context, volume, and sensitivity [13]. [PERSON] et al. proposed a secure multi-party summation protocol based on the multi-party summation function, verifying its efficiency through privacy analysis and efficiency evaluation, though the method has a high computational complexity [14]. [PERSON] et al. addressed blockchain privacy issues using SMC, designing a new protocol based on [PERSON]'s randomization technique to perform multiplication on encrypted secret shares, integrating the SMC protocol into Hyperledger Fabric, eliminating communication interactions between participants and the blockchain. However, most of the method's time was spent on communication, with performance not well guaranteed [15]. [PERSON] et al. formulated a secure privacy-preserving offloading scheme based on modified secret sharing and developed a morphology-based method to protect privacy data on IoT platforms, solving privacy leakage issues and the detection of worker honesty with low overhead [16]. Fully Homomorphic Encryption (FHE) technology provides a potential solution. FHE allows computations to be directly performed on encrypted data without the need for decryption, meaning conversational systems can process user requests without accessing any sensitive plaintext data. Thus, even if data leaks occur during processing or transmission, attackers cannot obtain any useful information, as they only see encrypted data. [PERSON] et al. proposed a new security concept--Fully Homomorphic Authenticated Encryption (FHAE)--employing an encryption-before-authentication pattern, but it was not very efficient [17]. To address this, they further introduced multi-dataset fully homomorphic authenticated encryption (MDFHAE), achieving privacy protection with high efficiency. [PERSON] et al. proposed a general framework for constructing multi-key FHE, evaluating ciphertexts through appropriate computational protocols, but the practicality of the model remains an issue [18]. [PERSON] et al. constructed Quantum Fully Homomorphic Encryption (QFHE) schemes against quantum computing, proposing two QFHE schemes: single-qubit point obfuscation and multi-qubit point obfuscation. Both of these were proved to be highly secure, but future needs for secure multi-party computation must be considered [19]. [PERSON] introduced an indistinguishability under chosen plaintext attacks (IND-CPA) secure FHE algorithm based on the difficulty of factoring in cloud computing, with the computational overhead of homomorphic evaluation at O(1) [20]. [PERSON] et al. presented a new private set intersection protocol based on the Gao tree red FHE scheme, which is simple and practical, but limited to two-party protocols and not very efficient [21]. [PERSON] et al. proved the insecurity of FHE applications--the Brakerski/Fan-Vercauteren (BFV) scheme--in IoT, confirming the potential security risks in the practical application of FHE; while FHE is secure, its protocols are prone to issues [22].