Privacy-Preserving Patent Matching Model

To address the problems of privacy leakage in patent matching and low matching accuracy, this paper proposes a hybrid homomorphic encryption model (HyMix-HE) for patent similarity matching. First, the user’s patent requirement vector is encrypted using CKKS, and floating-point similarities are computed in batch in the ciphertext domain of the requirements to obtain preliminary patent matches. Second, a δ safety band mechanism is designed to concentrate the samples that require exact review in the truly uncertain neighborhood, together with a secure-domain switching mechanism that converts ciphertexts from the CKKS approximate domain to the BFV fixed-point domain under minimal leakage. Finally, similarity computation over ciphertext is performed in BFV on these critical samples to achieve accurate patent review. Meanwhile, lightweight auditing is implemented on Hyperledger Fabric using commitment/hash techniques, enabling recomputation and accountability without exposing any plaintext. Based on modeled simulations and comparative experiments, the proposed method is evaluated against pure-CKKS and pure-BFV schemes. The results show that, while preserving privacy, the proposed model achieves a good balance between efficiency and accuracy and overall outperforms single-scheme baselines, thus effectively supporting privacy-preserving in patent trading scenarios.