: Quantum key distribution (QKD) is a promising secret key exchange protocol that can replace the currently used public key cryptography, which is vulnerable to quantum computers. The main limitation of QKD is communication distance constrained by the attenuation in optical fibers. One solution to this limitation is classical relay using trusted relay nodes. This technology extends the communication distance by securely relaying a secret key from one node to the next using QKD-shared keys. The trusted relay node assumption incurs significant security costs. Therefore, relaxing this trust assumption is desirable. This paper proposes a QKD protocol that relaxes the reliability of relay nodes to honest-but-curious and enables long-distance communication through multi-hop transmission, which has no constraint on the number of relay nodes. Our protocol utilizes BB84 quantum states to transmit a bit sequence via relay nodes, and employs a secure classical channel implemented with post-quantum cryptography (PQC) to prevent relay nodes from deriving the shared secret key. We then analyze the security of our protocol against a possible attack involving the inference of the bit sequence by relay nodes, which arises from the relaxation of the reliability. We finally show that our protocol ensures the security against honest-but-curious relay nodes by discarding insecure secret keys with a high inference probability.
Hiroki Yamamuro, Shusaku Uemura, Kazuhide Fukushima· Proceedings of the 23rd Inte...· 0 citations
: Fully homomorphic encryption (FHE) enables computations on ciphertexts without decryption. This property is expected to be utilized in AI with sensitive data. Although encryption improves the security of neural network inference, it incurs a significant computational overhead because all processes are executed under encryption. However, in many practical scenarios, not necessarily all the input features should be encrypted. Some features must be confidential whereas others can be disclosed to the model operator. Based on this observation, we propose a novel neural network architecture which partitions the input features into two types according to their secrecy. Our architecture decomposes a neural network into three modules to handle these two feature types efficiently. Public input features are processed without encryption whereas private input features are computed under encryption. We theoretically analyze the computational cost of our model and formulate the reduction rate in terms of the parameters. We also experimentally examine our model’s accuracy by comparing it to that of a standard model and demonstrate that our model reduces the computational costs by 50% for a certain parameter set while the accuracy degradation is limited.
Shusaku Uemura, Kazuhide Fukushima· Proceedings of the 23rd Inte...· 0 citations
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