Verifiable Privacy-Preserving Neural Network Inference via Multi-Key Homomorphic Encryption

With the proliferation of Internet of Things (IoT) devices, deep learning model inference that guarantees data privacy has attracted significant research interest. Secure inference based on homomorphic encryption offers a promising solution by ensuring rigorous data confidentiality. However, in typical cloud-based inference services, ciphertexts are encrypted under a single key, requiring all participants to share the same decryption key. This contradicts real-world scenarios where multiple participants (e.g., individual IoT devices or users) typically hold their own private keys. To address this issue, this paper designs a series of secure sub-protocols based on multi-key homomorphic encryption (MK-HE), enabling the computation of nonlinear functions while preserving data privacy across multiple keys. Furthermore, considering the potential malicious nature of cloud servers, we incorporate a commitment protocol to ensure the verifiability of inference results and prevent the server from returning incorrect outcomes. Theoretical and security analyses demonstrate the efficiency and privacy of our proposed protocol, which achieves lower computational time and reduced communication overhead without compromising security.