Gaimei Gao

To protect data privacy in cloud storage, sensitive data is commonly encrypted before being uploaded to a cloud storage server (CSS). This process challenges secure and efficient ciphertext retrieval. Identity‐based encryption with keyword search (IBEKS) enables secure ciphertext retrieval but remains vulnerable to internal adversary attacks, particularly algorithm substitution attacks (ASA) in SM9‐based searchable encryption schemes. Additionally, existing protocols lack proactive defense mechanisms, making them vulnerable to insider threats that compromise system integrity. To address these challenges, this article proposes SM9‐based Searchable Encryption with Cryptographic Reverse Firewall (SM9SE‐CRF), a novel scheme designed to resist internal attacks while ensuring efficient ciphertext retrieval. Initially, a re‐randomizable IBEKS framework is developed as the foundation for constructing the SM9 searchable encryption scheme, enhancing both security and performance. Furthermore, Cryptographic reverse firewalls are deployed at both the Key Generation Center (KGC) and user ends, which dynamically re‐randomize cryptographic parameters to mitigate risks posed by internal adversaries. The SM9SE‐CRF scheme is implemented using the JPBC library and evaluated through comprehensive security and performance analyses. Results demonstrate that SM9SE‐CRF effectively mitigates offline keyword guessing attacks and ASA threats from malicious insiders. Performance evaluations reveal that at a 128‐bit security level, SM9SE‐CRF achieves a 93% reduction in runtime compared to existing schemes, with the cryptographic reverse firewall adding merely 1.16% overhead. This minimal computational cost highlights the practical applicability of SM9SE‐CRF in privacy‐preserving cloud storage systems, particularly in enterprise data sharing, secure outsourced storage, and cloud‐based information retrieval applications.

Smart contracts are at the core of blockchain technology, but the cost of fixing their security vulnerabilities is high, making pre-deployment vulnerability detection crucial. Existing methods rely on fixed rules, which have limitations in accuracy and scalability, and their efficiency decreases with the complexity of the rules. Neural-network-based methods can identify some vulnerabilities but are inefficient in multi-vulnerability scenarios and depend on source code. To address these issues, we propose a multi-vulnerability-based smart contract detection method called RTMS. RTMS takes bytecode as input, disassembles it into opcodes, uses the gas consumed by the contract for data slicing, and extends the length of input opcodes through a layered structure. It employs a weighted binary cross-entropy (BCE) function to handle data imbalance and combines channel-sequence attention mechanisms to extract vulnerability correlation features. By using transfer learning, it reduces training parameters and computational costs. Our RTMS model can detect multiple vulnerabilities simultaneously, enhancing detection accuracy and efficiency. In experiments with 100,000 real contract samples, the model achieved a Jaccard coefficient of 0.9312, a Hamming loss of 0.0211, and an F1 score that improved by about 11 percentage points compared to existing models, demonstrating its superiority and stability.

Cloud storage is a mode of online storage in which data are stored on multiple virtual servers usually hosted by third parties. Cloud service providers manage and operate data storage as services. The major concern in cloud storage is the integrity of outsourced data. Many public auditing schemes which authorize the third party auditor (TPA) to check the integrity of outsourced data have been proposed. However, most of them are based on traditional public key cryptosystems. This paper proposes an efficient certificateless public auditing Scheme (CL‐PAS) in cloud storage. In the process of challenge and auditing, CL‐PAS ensures that malicious cloud servers cannot forge auditing evidences to deceive TPA to pass the verification, and prevents curious TPA from recovering original data from auditing evidences. So it strengthens privacy preserving. Security analysis shows that CL‐PAS scheme meets the predetermined security requirements. The simulation and performance analysis results show that, compared with similar schemes, CL‐PAS scheme has the higher efficiency.