To improve data availability and durability, cloud users would like to store multiple copies of their original files at servers. The multi-copy auditing technique is proposed to provide users with the assurance that multiple copies are actually stored in the cloud. However, most multi-replica solutions rely on Public Key Infrastructure (PKI), which entails heavy overhead of certificate computation and management. In this paper, we propose an efficient multi-copy dynamic integrity auditing scheme by employing certificateless signatures (named MDSS), which gets rid of expensive certificate management overhead and avoids the key escrow problem in identity-based signatures. Specifically, we improve the classic Merkle Hash Tree (MHT) to achieve batch updates for multi-copy storage, which allows the communication overhead incurred for dynamics to be independent of the replica number. To meet the flexible storage requirement, we propose a variable replica number storage strategy, allowing users to determine the replica number for each block. Based on the fact that auditors may frame Cloud Storage Servers (CSSs), we use signature verification to prevent malicious auditors from framing honest CSSs. Finally, security analysis proves that our proposal is secure in the random oracle model. Analysis and simulation results show that our proposal is more efficient than current state-of-the-art schemes.

Internet of Things (IoT) devices upload their data into the cloud for storage because of their limited resources. However, cloud storage data has been subject to potential integrity threats, and consequently auditing techniques are demanded to ensure the integrity of stored data. Unfortunately, existing auditing approaches require owners to undertake expensive tag calculations, which is unsuitable for resource-constrained IoT devices. To resolve the issue, we present a F air C loud A uditing proposal by employing the B lockchain (FCAB). We combine certificateless signatures with the designed dynamic structure to constructively offload the cost of tag computation from the IoT device to the introduced fog node, significantly reducing the local burden. Considering that fog nodes may behave dishonestly during auditing, FCAB enables the IoT device to verify the audit result's authenticity by extracting reliable checking records from the blockchain, thereby achieving auditing fairness, which ensures that the honest cloud and fog node will gain the corresponding reward. Finally, FCAB is proved to satisfy tag unforgeability, proof unforgeability, privacy preserving, and auditing fairness. Experiment evaluations affirm that FCAB is computationally and communicationally efficient and retains a smaller and fixed computation locally at the data processing stage (mainly including tag computation) than existing auditing methods.

With the rapid development of cloud computing, more and more users tend to store their data remotely to the cloud. Taking into account data security and resource utilization comprehensively, in addition to providing users with basic remote data integrity verification, cloud servers also need to conduct redundancy checks. However, current deduplication schemes primarily focus on static file-level data and auditing processes, rendering them inadequate for managing resources with dynamic attributes. In this paper, we propose a fine-grained deduplication and dynamic auditing model (FiDD) for cloud storage to address these challenges. FiDD utilizes homomorphic verifier-based data tags to seamlessly integrate deduplication and auditing processes, allowing both block-level and file-level deduplications. Additionally, FiDD employs doubly linked lists and multi-set hash functions to enhance the efficiency of data updates. The security of FiDD is validated through rigorous security proofs, while its efficiency is demonstrated through comprehensive experimental analysis. The experiments demonstrate an average improvement of at least 35% in audit efficiency and at least 50% in dynamics efficiency. Consequently, FiDD enhanes the security of data management in cloud computing while improving its overall efficiency.

As cloud storage is extensively utilized in the contemporary digital age, assuring data integrity and conserving cloud storage space has become a priority for all. However, existing cross-user deduplication audit schemes conflict with the pay-as-you-go model, causing unnecessary costs and violating data isolation. Moreover, retaining a single copy of identical data across multiple users introduces maintenance challenges during data operations. To address these issues, we propose a new blockchain-enabled efficient deduplication and mixed auditing scheme which intricately integrates Message-Locked Encryption (MLE) to construct Homomorphic Verifiable Tags (HVTs), enabling deduplication without exposing confidential data. Our scheme supports single-user deduplication at both block and file levels, as well as plaintext-ciphertext mixed auditing, thereby preventing redundant payments while preserving data isolation to simplify maintenance during data operations and ownership transfers. By employing Elliptic Curve Cryptography (ECC) to encrypt keys and storing the encrypted keys on the blockchain, we ensure data confidentiality while reducing the burden of local key management. Leveraging blockchain-based smart contracts, we further design a self-auditing mechanism that eliminates reliance on trusted third-party auditors. Moreover, our scheme embraces dynamic data operations through an optimized Merkle Hash Tree (MHT) and enables secure cloud data ownership transfer via identity verification. Finally, we prove the correctness and security of our scheme and evaluate its performance through experiments and comparisons with state-of-the-art works, demonstrating its efficiency, particularly in the data upload phase.

Internet of Things (IoT) devices can utilize deep learning to boost their intelligence, but also suffer from the long model training process. IoT devices thus may reuse public pre-trained models to expedite the training through transfer learning. However, pre-trained models may be subject to model-reuse attacks initiated by malicious deep learning servers, causing models to misclassify targeted data, which poses a threat to the security of IoT devices. In this work, we propose a new model usability detection scheme, the Defense against Model-Reuse Attacks (DMRA), suitable for IoT scenarios. DMRA employs a variant of Lagrange’s mean value theorem to reverse-check the model, which is computationally efficient, thus, suitable for resource-constrained devices. Experimental evaluations on different datasets firstly validate that model-reuse attacks can attack models in federated learning. And then demonstrate that DMRA detects such insidious attacks with up to 80% success rate at a lightweight computational cost.