TFMD: General and Fast Secure Neural Network Inference Framework With Threshold FHE

Résumé fourni par la source

Secure neural network inference is the privacy-preserving inference method that protects the model parameters and user’s private input. Previous works have constructed two-party, three-party and four-party secure inference schemes. However, these schemes allow only one corrupted party. Also, the interaction protocol between different parties is customized based on the number of participants. If the number of participants increases or decreases, the protocol needs to be redesigned. Another problem is that current protocols for non-linear functions still have large computation overhead. In this work, we present TFMD, a general and fast secure neural network inference framework with semi-honest security. TFMD is built based on threshold fully homomorphic encryption (FHE), and is suitable for the outsourced computation scenario. Concretely, TFMD designs general secure computation protocols for non-linear functions. Our protocols support arbitrarynparticipants, and allow at mostn– 1 corrupted parties. Further, TFMD constructs a novel secure neural network inference framework. TFMD employs FHE with computation-friendly coefficient encoding to quickly calculate linear functions, and employs our proposed protocol to calculate ReLU. Experiments illustrate that TFMD is both efficient and scalable. Even in the three-party setting, the online phase of our inference is 2.1× faster than CrypTFlow (S&P’20).

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