Cloud computing, which provides a brand-new service model, has become an important infrastructure in the information age, and has been widely used in numerous fields. The Key-Policy Attribute-Based Encryption (KP-ABE) scheme allows the encrypted data with fine-grained access control in the cloud environment. However, achieving large-scale user revocation in the application scenario of KP-ABE becomes one of the thorny problems. Furthermore, the computation and communication costs of the previous user revocation schemes were generally high, especially when a large number of users were revoked. To address these problems, an enhanced high-performance user-revocable KP-ABE scheme combined with the puncture method was proposed. In this article, the user could be revoked by the fine-grained restriction policy. When revoking the user, the cloud would run the puncture algorithm to embed the restriction policy defined by the data owner into the ciphertext. This method could effectively omit the re-encryption and key updating processes, by which the computation and communication overhead of the user revocation are efficiently reduced, and the user revocation becomes more flexible and efficient. Moreover, the Chosen-Plaintext Attack (CPA) security proof and extensive simulation results demonstrate the reliability and efficiency of the proposed scheme for user revocation in a cloud environment.

Ciphertext-Policy Attribute-Based Searchable Encryption (CP-ABSE) is one of the most suitable encryption mechanisms in cloud environments for its fine-grained access structure and keyword retrieval capability over the ciphertext. However, in the CP-ABSE schemes, guaranteeing the forward security of the outsourced cloud data and securely deleting those no longer needed data without relying on the cloud are challenging problems. To handle such challenges, we propose a Puncturable CP-ABSE (Pun-CP-ABSE) scheme that achieves self-controlled data deletion with a fine-grained access structure under the searchable mechanism. The data owner punctures the trapdoor to accomplish the data deletion. Then, the deletion process does not need to communicate with a trusted third party and can guarantee forward security. After the puncturation, the cloud server can no longer search for the corresponding ciphertext. Furthermore, we prove the Pun-CP-ABSE scheme is secure against the Chosen-Plaintext Attack (CPA) and Chosen-Keyword Attack (CKA). We have also implemented the Pun-CP-ABSE scheme to show its efficiency and feasibility.

Electronic Medical Records (EMR) are the essential sensitive personal data that is shared between patients and doctors through the semi-trusted E-health cloud. In the real application, Multi-Authority Ciphertext-Policy Attribute-Based Searchable Encryption (MA-CP-ABSE) is suitable to protect the security of the EMR for it possesses fine-grained access permission, effcient key management and retrieval function over the encrypted data. However, previously proposed MA-CP-ABSE schemes are commonly restricted by the central authority, which is required by all attribute authorities in some operations like generating users’ secret keys. To deal with this issue, we proposed a Decentralized MA-CP-ABSE (DMA-CP-ABSE) scheme. In our scheme, any attribute authority can become an independent authority to generate a secret key, which is no longer controlled by the central authority. Furthermore, a single keyword search will produce many disrelated search results. For this, we enhance the DMA-CP-ABMSE scheme by implementing a multi-keyword search function to improve the search accuracy. Besides, to handle the dynamic changing of access permission, we have designed the attribute revocation methods. Finally, we process the formal security analysis to demonstrate our scheme is secure under the Chosen-Keyword Attack (CKA) and implement experiments to show its effciency and feasibility.

Given growing concerns regarding data security, we develop an enhanced Advanced Encryption Standard (AES) by incorporating chaotic mapping techniques and implement it within a secure data transmission scheme, thereby strengthening protection mechanisms for both data storage and transmission processes. First, we developed a new 2D enhanced hyperchaotic map (2D-EHM) by combining classical 1D chaotic maps and conducted dynamic testing and analysis using bifurcation diagrams, phase diagrams, Lyapunov exponent graphs, and sample entropy. The results demonstrate that the 2D-EHM exhibits stronger chaotic properties compared to existing chaotic maps. Subsequently, we optimized each step of the AES algorithm by incorporating the proposed chaotic map. The enhanced AES achieves higher security at every stage of the encryption process and utilizes two different strong S-Boxes, effectively addressing the issues related to fixed points, reverse fixed points, and short periodic cycles. Based on this, we designed a secure data transmission scheme. Finally, we conducted a security analysis of the data encryption algorithm, and the results confirm the feasibility and effectiveness of our approach.

Ciphertext-Policy Attribute-Based Searchable Encryption (CP-ABSE) is essential for fine-grained search authorization in multi-user data sharing, with notable practical application value. However, existing CP-ABSE schemes have prominent performance problems. Their encryption and search overhead rise linearly as the number of attributes increases, leading to obvious search latency. Furthermore, it is still difficult to balance the strong expressiveness of Linear Secret Sharing Scheme (LSSS) access structures with system scalability. This issue limits the large-scale practical deployment of those schemes. To solve these problems, this paper designs an efficient CP-ABSE scheme that retains LSSS-based fine-grained access control capability. This scheme simultaneously takes into account both attribute scale growth and performance degradation, and the computational efficiency gets better while ensuring access control expressiveness. The security proof shows that it can resist Chosen Keyword Attacks (CKA). Experimental results show that compared with existing schemes, our scheme reduces the cost of key generation by 40%–50%, encryption by 25%–40%, trapdoor generation by 50%–60%, and search operation by 30%–45%. In addition, its performance overhead grows more slowly with the increase of attributes, and the search latency remains stable. These advantages confirm its good scalability and high practicability in large-scale user data sharing scenarios.