Personal Credit Evaluation Model Based on Homomorphic Encryption

As the financial industry continues to open up, numerous multi-party data modeling cases have emerged. Given the high value and confidentiality of financial data, the modeling process faces significant security risks, including the potential leakage of critical financial information, such as user privacy. Ensuring that data remains “usable but not visible” during the modeling process has become a critical technical challenge in this field. Privacy computing seeks to enable data analysis and computation without external exposure, transforming and unlocking data value while fully safeguarding both data and privacy. Homomorphic encryption is a key privacy computing technology that encrypts raw data and allows specific operations to be performed on the ciphertext. The decrypted results of these operations are equivalent to those performed on plaintext data. Compared to other privacy protection methods, such as differential privacy, federated learning, and k-anonymity, homomorphic encryption stands out as a provably secure cryptographic solution for cloud-based privacy computing. It enables fraud detection models in cloud computing to perform encrypted polynomial calculations on ciphertext data without accessing users' real data, making ciphertext-based fraud detection a prominent research area. To address the issue of privacy data leakage in multi-party data model construction, this paper proposes a novel secure multi-party computation model based on homomorphic encryption. We introduce a homomorphic encryption-based machine learning algorithm, evaluating its accuracy and efficiency on real datasets, demonstrating its effectiveness and potential for practical applications.