Edge computing paradigm deploys infrastructures on users' edge side, enabling data to be stored in edge storage systems. Thus edge computing finds a balance between remote cloud storage with high latency and local storage with limited resources. However, the frequent update of edge data increases the probability of data corrupting. To ensure availability and integrity of edge data without affecting future use, this paper proposes a certificateLess edge recoverable data auditing (CL-ERDA) approach with corruption localization. First, the certificateless network coding signature in CL-ERDA is utilized to generate the homomorphic authenticator, addressing public key certificate management issues while avoiding insecure key escrow problems. Then, CL-ERDA designs a two-phase auditing method, where only file-level aggregation proofs are respond in first phase of external check and the second phase of self-check is launched only when damaged data is detected, reducing communication overhead of the auditing. Moreover, CL-ERDA combines hierarchical check method with batch localization method to achieve locating efficiently all the corrupted data at one time rather than locating only one corrupted data at once. Finally, security analysis shows that the proposed scheme resists various attacks and supports the unforgeability of authenticators. Experimental results demonstrate that CL-ERDA is efficient in auditing and corruption localization.

Efficient power management in smart grids relies on regularly obtaining the electricity usage of each user. However, the aggregation of the electricity usage data may expose user privacy. At present, most existing solutions aggregate the electricity data of the entire user set, which cannot meet the fine-grained requirements of the control center. Therefore, this paper proposes a verifiable privacy-preserving multisubset dynamic billing data aggregation scheme. Firstly, we divide the electricity data into k consecutive subsets and set dynamic pricing rules, such as pricing within a certain range as q1and exceeding it as q2. Then, the smart meter encrypts the data using the Paillier cryptographic system with the corresponding subset of parameters and uploads them to the aggregation equipment. After the aggregation equipment completes the users’ data aggregation, the ciphertext C is sent to the control center. This process enables the control center to check data integrity and obtain the total number of people, electricity consumption, and costs for different ranges of electricity in a period of time at once, without the need to obtain data for individual users. Analysis and experiments show that this scheme can resist attacks from powerful adversaries on the transmission channel and has practicality and effectiveness.

Internet of things (IoT) has emerged as a promising technology that can be widely used in various industries to realize real-time information collection, so as to improve production efficiency and reduce running costs. By combining the technology of IoT, maritime transportation systems (MTS) can prevent vessels collision, improve the efficiency of maritime transportation and reduce the loss of revenue for ports and shipbuilders. The large amount of real-time data generated in IoT-enabled MTS can be efficiently utilized to predict the future trajectories and hotspots of vessels on the sea combined with historical data. However, the maritime traffic data in MTS cannot be effectively processed in traditional big data analysis methods, and the integrity of it needs to be checked before being used to achieve the prediction of trajectories and high-density areas of vessels. In this paper, we propose a flexible data integrity checking scheme with original data recovery in IoT-enabled MTS. In the proposed scheme, the data blocks of vessels are encoded based on the technology of erasure coding. To ensure the availability of the historical data, the existence and the integrity of the data elements stored in the cloud can be checked. Moreover, the original data blocks can be recovered efficiently if the encoded data elements have been corrupted or deleted. Security analysis demonstrates that the proposed scheme can be proved to be correct and is secure against malicious attacks. Performance analysis shows that our scheme is more efficient than the previous schemes.

Incompletely predictable vehicular ad hoc networks is a type of networks where vehicles move in a certain range or just in a particular tendency, which is very similar to some circumstances in reality. However, how to route in such type of networks more efficiently according to the node motion characteristics and related historical big data is still an open issue. In this paper, we propose a novel routing protocol named trustworthiness evaluation-based routing protocol (TERP). In our protocol, trustworthiness of each individual is calculated by the cloud depending on the attribute parameters uploaded by the corresponding vehicle. In addition, according to the trustworthiness provided by the cloud, vehicles in the network choose reliable forward nodes and complete the entire route. The analysis shows that our protocol can effectively improve the fairness of the trustworthiness judgement. In the simulation, our protocol has a good performance in terms of the packet delivery ratio, normalized routing overhead and average end-to-end delay.

With the fast growing demands for the big data, we need to manage and store the big data in the cloud. Since the cloud is not fully trusted and it can be accessed by any users, the data in the cloud may face threats. In this paper, we propose a secure authentication protocol for cloud big data with a hierarchical attribute authorization structure. Our proposed protocol resorts to the tree-based signature to significantly improve the security of attribute authorization. To satisfy the big data requirements, we extend the proposed authentication protocol to support multiple levels in the hierarchical attribute authorization structure. Security analysis shows that our protocol can resist the forgery attack and replay attack. In addition, our protocol can preserve the entities privacy. Comparing with the previous studies, we can show that our protocol has lower computational and communication overhead.

With the rapid development of cloud services, the resources-constrained enterprises and individuals can outsource the huge sensitive data into the Cloud Service Providers (CSPs) who fully control the data physically. Since CSPs are not fully trusted, it is essential to protect the integrity and confidentiality of users' data. Plenty of researchers have devoted considerable attention to solve this issue in the last decade such as various PDP and POR schemes. In this paper, we propose an algebraic signature-based data integrity auditing scheme that ensures the cloud data integrity and confidentiality with batch auditing. Moreover, one advantage of the scheme is that it can also support data dynamics by using only one cloud server. The security analysis shows that our construction can achieve the desired security properties. We also provide the simulation results of the dynamic operations on different numbers of data blocks and sub-blocks, which show that our scheme is efficient for real-world applications.