To ensure the reliability and integrity of data in the cloud storage server, some scholars provided various data integrity auditing schemes. However, the most existing data integrity auditing schemes only support the static data and may be unsuitable for the dynamic operations of data. To overcome this difficulty, we propose a fuzzy identity-based dynamic auditing of big data, which combines the structure of the Merkle hash tree (MHT) with the Index logic table (ILT). Our scheme not only performs the dynamic operations of data block in the ILT, namely modification, insertion and deletion, but also efficiently executes dynamic operations of the ILT on the structure of the MHT. We also elaborate the security, characteristics and performance analysis of the proposed scheme separately. The analysis results show that the proposed scheme costs less time than the structure of the original MHT to generate the root node hash value during the metadata generation phase and update the root node hash value during the dynamic operations. Furthermore, when users store the new ILT in local storage, they require lower communication cost to update root node hash value than users without storing the ILT, and fewer interactions between the cloud storage server and users in the dynamic operations process. INDEX TERMS Big data, cloud storage, data integrity, dynamic operation, fuzzy identity-based.

Digital video forensics plays a vital role in judicial forensics, media reports, e-commerce, finance, and public security. Although many methods have been developed, there is currently no efficient solution to real-life videos with illumination noises and jitter noises. To solve this issue, we propose a detection method that adapts to brightness and jitter for video inter-frame forgery. For videos with severe brightness changes, we relax the brightness constancy constraint and adopt intensity normalization to propose a new optical flow algorithm. For videos with large jitter noises, we introduce motion entropy to detect the jitter and extract the stable feature of texture changes fraction for double-checking. Experimental results show that, compared with previous algorithms, the proposed method is more accurate and robust for videos with significant brightness variance or videos with heavy jitter on public benchmark datasets.

Cloud‐based medical storage system is becoming popular. Cloud server is curious about the private information of stored cases, and the integrity of outsourced data has become increasingly concerned. Numerous public auditing protocols for the outsourced data into the cloud server were proposed. Unfortunately, in the existing protocols, some of them may neglect security to improve efficiency. Recently, Li et al. proposed an efficient privacy‐preserving public auditing protocol for cloud‐based medical storage system (EPPAP), which improved the communication efficiency by storing some of data owner's data in third part auditors, however we find that the protocol is vulnerable to collusion attack and replace attack. In this paper, we analyze the security attacks of the existing protocol, and further propose an improved public auditing protocol for secure data storage in cloud‐based medical storage system (IPAPS). In addition, we give the formal security proof and analyze the performance of our proposed protocol. The security proof shows our protocol takes into account integrity, collusion attack and replace attack at the same time. The simulation experiments in our performance analysis show that the proposed protocol is practical.

No abstract