In distributed large-scale intelligent computing, efficient collaboration between the cloud and edge devices is essential to provide rapid data access for a vast number of users, which is crucial for optimal data storage services. The edge servers are utilized to meet the data access requirements of adjacent group users. Numerous data auditing schemes for cloud computing have been proposed to protect user data integrity and avoid the threat of data leakage, loss, or tampering. However, secure cloud-edge storage of group users remains to be further studied. This paper proposes a novel blockchain-assisted blinded data auditing scheme for cloud-edge systems. Firstly, blockchain technology is utilized to monitor the activities of a semi-trusted third-party auditor (TPA). Secondly, user identifiers are utilized to trace the malicious behaviors in the systems. In addition, a data blinding algorithm is utilized to ensure that the raw data is unavailable to a curious TPA. Both security analysis and performance assessment demonstrate that this scheme conducts data auditing for cloud-edge systems with reliability and efficiency.

As cloud computing gains widespread adoption, cloud storage services have become the primary means of data management for users. Authenticated data structures (ADS) are a novel computational model designed to address data authentication problems in distributed environments. With the growing demand for robust data security, vulnerability detection in storage systems has become a critical area of focus to ensure resilience against potential threats. However, traditional ADS, while ensuring consistency between cloud data and source data, have limitations in handling dynamic data operations on multiple types of files, storage space expansion, and single-point failure issues. To tackle these issues, this paper proposes a blockchain-assisted classifiable data auditing scheme with dynamic operations. First, trapdoor hash functions are used to construct a binary tree. During dynamic data operations, the impact of hash updates is confined to a subset of nodes, ensuring global stability and reducing computational resource consumption. Second, innovative data structures and verification mechanisms are introduced, reducing the risk of single-point failures by decentralizing the dependency on verification paths. Finally, data types are confirmed based on data identifiers, and corresponding path information is recorded, enabling efficient and rapid dynamic operations on specific types of files within multi-source data. Both security analysis and performance assessment demonstrate that BCDAS conducts data auditing with reliability and efficiency.

As a new generation of electricity system, smart grid significantly improves electricity services’ efficiency, reliability, and sustainability. The smart meters, which are the essential terminals, help establish two-way communication between users and electricity providers. While enjoying the convenience of smart meters, users face many challenges. On the one hand, malicious adversaries could attack the smart meters and thus steal the users’ privacy. On the other hand, the computational overhead of electricity data verification is high for lightweight smart meters. To address above issues, a lightweight authentication and group key management scheme is proposed. In the proposed scheme, the physical properties of the Physical Unclonable Function (PUF) are exploited to defend against external attacks from adversaries. Moreover, the Chinese Remainder Theorem (CRT) is used to broadcast the updated group keys for the legitimate smart meters in the community. In addition, the aggregated signature is utilized to reduce the overhead of the data verification. Finally, the Random Oracle Model (ROM) is used to demonstrate that the proposed scheme meets many security requirements. Performance analysis shows that the proposed scheme is more suitable for smart grid compared to previous schemes.

The various data collected by urban sensor devices need a huge storage space. The cloud’s powerful calculation ability provides decent performance support and reduces the overhead on local storage. There are many kinds of data collected by urban sensor devices, and the same kind of sensors often need to upload large numbers of files together, which are usually private, so secure cloud storage in group members is essential. However, the cloud may try to modify or hide data for its own benefit, which requires the detection of a third-party auditor. This paper puts forward a new public audit scheme. Some changes have been made to the way of generating the private key, the parameters of key generation come from the feature information set of the same sensor. When the two information sets are close enough, the correctness of the authenticator generated by this file can be verified. When the sensor is revoked due to damage or loss, this scheme will effectively revoke and regenerate the private key with less overhead, thus preventing the leakage of data privacy. This also avoids the huge overhead caused by re-downloading data to generate authenticators in traditional schemes. In addition, our scheme is effective in tracking malicious members and reducing the computing overhead of the group. Finally, the experimental results show the effectiveness and practicality of the proposed scheme.