Proxy re-encryption (PRE), with the unique ciphertext transformation ability, enables various ciphertext authorization applications to be implemented efficiently. However, most existing PRE schemes mainly focus on access authorization while ignoring the situation where the key needs to be changed and the ciphertext needs to be evolved, making the scheme's practicability and security inadequate. Moreover, the few schemes that simultaneously combine ciphertext authorization, key update, and ciphertext evolution are not satisfactory in terms of security. For solving this problem, based on Xiong et al.'s scheme, this paper proposes an improved revocable and identity-based conditional proxy re-encryption scheme with ciphertext evolution (RIB-CPRE-CE) for secure and efficient cloud data sharing. The proposed scheme inherits the characteristics of multi-use, constant ciphertext length, fine-grained authorization, collisionresistance security, and chosen ciphertext attack (CCA) security from the original method. Also, it supports updating ciphertext to adapt to the new key after changing the identity (key) or achieves authorization revocation by evolving ciphertext. Two new algorithms, URKeyGen and UpReEnc, have been integrated into the original delegation scheme to support ciphertext evolution. The formal definition, security model, concrete construction, and security analysis of RIB-CPRE-CE have been presented. The comparison and analysis show that the proposed scheme is practical and secure. Although it adds a ciphertext evolution function for supporting key update and delegation revocation, its efficiency and security are not reduced. The proposed scheme can also be used in other access authorization systems that need to change the key or revoke the authorization. It has certain practicability and security.

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5G networks have been experiencing challenges in handling the heterogeneity and influx of user requests brought upon by the constant emergence of various services. As such, network slicing is considered one of the critical technologies for improving the performance of 5G networks. This technology has shown great potential for enhancing network scalability and dynamic service provisioning through the effective allocation of network resources. This paper presents a Deep Reinforcement Learning-based network slicing scheme to improve resource allocation in 5G networks. First, a Contextual Bandit model for the network slicing process is created, and then a Deep Reinforcement Learning-based network slicing agent (NSA) is developed. The agent’s goal is to maximize every action’s reward by considering the current network state and resource utilization. Additionally, we utilize network theory concepts and methods for node selection, ranking, and mapping. Extensive simulation has been performed to show that the proposed scheme can achieve higher action rewards, resource efficiency, and network throughput compared to other algorithms.

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