Compared with the traditional system, cloud storage users have no direct control over their data, so users are most concerned about security for their data stored in the cloud. One security requirement is to resolve any threats from semi-trusted key third party managers. The proposed data security for cloud environment with semi-trusted third party (DaSCE) protocol has solved the security threat of key managers to some extent but has not achieved positive results. Based on this, this paper proposes a semi-trusted third-party data security protocol (ADSS), which can effectively remove this security threat by adding time stamp and blind factor to prevent key managers and intermediaries from intercepting and decrypting user data. Moreover, the ADSS protocol is proved to provide indistinguishable security under a chosen ciphertext attack. Finally, the performance evaluation and simulation of the protocol show that the ADSS security is greater than DaSCE, and the amount of time needed is lower than DaSCE.
The advancement of deep neural networks (DNNs) has prompted many cloud service providers to offer deep learning as a service (DLaaS) to users across various application domains. However, in current DLaaS prediction systems, users’ data are at risk of leakage. Homomorphic encryption allows operations to be performed on ciphertext without decryption, which can be applied to DLaaS to ensure users’ data privacy. However, mainstream homomorphic encryption schemes only support homomorphic addition and multiplication, and do not support the ReLU activation function commonly used in the activation layers of DNNs. Previous work used approximate polynomials to replace the ReLU activation function, but the DNNs they implemented either had low inference accuracy or high inference latency. In order to achieve low inference latency of DNNs on encrypted data while ensuring inference accuracy, we propose a low-degree Hermite deep neural network framework (called LHDNN), which uses a set of low-degree trainable Hermite polynomials (called LotHps) as activation layers of DNNs. Additionally, LHDNN integrates a novel weight initialization and regularization module into the LotHps activation layer, which makes the training process of DNNs more stable and gives a stronger generalization ability. Additionally, to further improve the model accuracy, we propose a variable-weighted difference training (VDT) strategy that uses ReLU-based models to guide the training of LotHps-based models. Extensive experiments on multiple benchmark datasets validate the superiority of LHDNN in terms of inference speed and accuracy on encrypted data.
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