## 1 Introduction  
In the current era of big data, the widespread application of data has brought revolutionary changes to societal development [1]. However, this also presents significant challenges for personal privacy protection [2]. In various application scenarios, such as facial recognition [3] and financial computing [4], ensuring the safety of personal privacy during the collection and processing of user data has become a critical issue. The leakage of personal information can lead to numerous security risks [5; 6], including but not limited to identity theft, financial loss, and personal privacy invasion [7]. Thus, developing a framework capable of effectively protecting user privacy across all scenarios has become a pivotal research direction in the field of data security. Traditional data privacy protection methods are mainly based on encryption algorithms, such as homomorphic encryption [8] and fully homomorphic encryption [9]. These methods perform secure computations by encrypting data, theoretically ensuring data privacy during processing. However, these technologies often suffer from low computational efficiency and complex implementation, making it difficult to meet the growing demands for data processing. Additionally, privacy protection techniques based on noise addition, such as differential privacy, have been widely applied in fields like statistical data release [10]. Yet, balancing privacy protection and data usability in practice remains a significant challenge. In the context of big data, especially in sensitive scenarios like facial recognition and financial computing, the issues of personal information security and privacy are particularly prominent. For example, in the widespread application of facial recognition technology in security monitoring and payment verification, using these personal biometric features without infringing on individual privacy is a critical issue to be addressed. [PERSON] et al. [11] proposed a privacy-preserving technique called \"controlled information release\" to prevent privacy leakage when providing biometric information to untrusted third-party servers. They masked the original facial images during identity recognition to prevent biometric leakage and proposed a new privacy-preserving facial recognition protocol called PEEP (Privacy-Preserving Eigendecomposition-based Encoding of Faces). PEEP perturbs facial features using different privacy levels and stores only the perturbed data on third-party servers or runs standard facial recognition algorithms. Experimental results showed that under standard privacy settings, PEEP achieved classification accuracy of approximately 70-90%. However, despite PEEP's ability to protect personal biometric features through perturbation, achieving privacy protection without compromising system performance and accuracy remains a key challenge. Although PEEP can achieve 70-90% classification accuracy under standard privacy settings, maintaining this performance in more complex or dynamically changing real-world applications may be more challenging. [PERSON] et al. [12] developed technologies and computational models that ensure personal privacy while promoting the application of facial recognition technology in various scenarios. [PERSON] et al. [13] proposed an efficient and secure outsourcing design for facial recognition data sensitivity and edge server security challenges. Experimental results indicated that this technology effectively protected critical privacy information. Similarly, in the financial services sector, protecting customer transaction data and personal financial information is crucial, as any information leakage could lead to credit risks and financial losses. [PERSON] et al. [14] studied the complex relationship between financial technology and data privacy laws, exploring the impact of data privacy laws on fitted, regulatory compliance, open access challenges, and opportunities to promote trust and innovation in the digital financial ecosystem.