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Homomorphic Encryption for Deep Learning
1 paper with code · 0 benchmarks · 0 datasets archive 2025-07-28
Homomorphic Encryption is the idea where input is encrypted by a client using a public key and the data is sent to the server. The server can perform computation on the encrypted data without knowing the contents of the encrypted data, and the final encrypted result can only be decoded once it is received by the client who owns the private key for the decryption.
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16 May 2025 1 repository listedBy replacing standard non-linearities with polynomial activations, Polynomial Neural Networks (PNNs) are pivotal for applications such as privacy-preserving inference via Homomorphic Encryption (HE).
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