Batch-oriented Element-wise Approximate Activation for Privacy-Preserving Neural Networks

arXiv:2403.10920v1 Announce Type: new
Abstract: Privacy-Preserving Neural Networks (PPNN) are advanced to perform inference without breaching user privacy, which can serve as an essential tool for medical diagnosis to simultaneously achieve big data utility and privacy protection. As one of the key techniques to enable PPNN, Fully Homomorphic Encryption (FHE) is facing a great challenge that homomorphic operations cannot be easily adapted for non-linear activation calculations. In this paper, batch-oriented element-wise data packing and approximate activation are proposed, which train …

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