Homomorphic Masking
We intend to explore partially homomorphic encryption (PHE) schemes, such as Paillier, which are more efficient than fully homomorphic encryption for specific operations like addition in AI workloads.
Partially Homomorphic Encryption (PHE)
Research will optimize PHE for matrix operations critical to neural networks, reducing computational costs within Substrate's weight-based execution model.
This approach allows computations on encrypted data without decryption, ensuring that sensitive data remains protected throughout the process, even during intermediate steps of AI training verified through Substrate's verification infrastructure. For instance, in a neural network, PHE could enable secure addition of encrypted weights, preserving privacy while maintaining computational efficiency within Substrate's runtime environment.
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