A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks. (arXiv:2308.05636v1 [cs.CR])

Machine learning (ML) is widely used today, especially through deep neural
networks (DNNs), however, increasing computational load and resource
requirements have led to cloud-based solutions. To address this problem, a new
generation of networks called Spiking Neural Networks (SNN) has emerged, which
mimic the behavior of the human brain to improve efficiency and reduce energy
consumption. These networks often process large amounts of sensitive
information, such as confidential data, and thus privacy issues arise.
Homomorphic encryption (HE) offers a solution, …

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