Attribute-based searchable encryption (ABSE) is a promising encryption mechanism for sharing outsourced encrypted data in clouds, allowing fine-grained access control over data while searching for encrypted data. However, the access policy in the most existing ABSE schemes exists in plaintext, which could expose sensitive information about legitimate data users. Moreover, such schemes delegate complex search operations to a cloud server, which can lead to data tampering and even untrusted results, and single point of failure. In this paper, we propose a blockchain-based anonymous attribute-based searchable encryption scheme for data sharing (BADS). First, attributes of the access policy are hidden, thus providing confidentiality to the set of attributes that satisfy the access policy. Then combining ABSE with blockchain have features of tamper-proof, integrity verification and non-repudiation. In particular, information such as secure index is stored in blockchain, while encrypted data is stored in a distributed system called the InterPlanetary File System (IPFS) to avoid single point of failure. Finally, BADS supports the matching algorithm that perform a fixed number of pairing operations before searching algorithm. We analysis security and evaluate performance to show the efficiency and practicability of BADS.

With the popularity of cloud storage, increasing users begin to outsource data to the cloud. In order to resist possible data analysis for centralized outsourced data and improve the fault tolerance, users prefer to distribute data to cloud servers of different cloud service providers. However, once the data have been outsourced, it will be out of user’s control and many security issues may occur, such as outsourced data being illegally tamper with, or rarely accessed data being secretly deleted. In this article, we propose a decentralized self-auditing scheme for multi-cloud storage, called DSAS. First, based on the symmetric balanced incomplete block design, DSAS achieves integrity verification for outsourced data via the interactions of cloud servers and the auditing costs are shared by the participating CSs. Second, DSAS can locate misbehavior cloud server with low computation costs, and resist denial of service attack initiated by malicious cloud servers which attempts to destroy the audit. Third, DSAS can recover the corrupted data without fetching data, and support the revocation of cloud servers and batch auditing. Finally, security proof and function evaluation show that DSAS has comprehensive security and functionality, and performance simulations and experiment results show that DSAS is efficient.

With the rapid development of Internet of Things, the related data are growing explosively. However, IoT devices have limited storage and computing capabilities so that they cannot deal with massive data storage and computing locally. The integration of IoT and cloud is regarded as an effective solution to the above issue, i.e., IoT devices outsource collected data to cloud to enjoy powerful storage and computing resources. Because the data stored in cloud are out of the control of IoT users, some security risks need to be addressed in advance. In this paper, we propose a public data integrity verification scheme, called AIVCI, to check the data integrity for Cloud-IoT scenarios. Firstly, based on algebraic signature and homomorphic hash function, AIVCI can efficiently complete data auditing. Secondly, AIVCI adopts blind technology to prevent the privacy leakage of IoT data and further protect the privacy of IoT users. Thirdly, batch auditing is implemented to improve auditing efficiency and meet realistic demands for Cloud-IoT scenarios. And a new data structure named Improved Divide and Conquer Table (ID&CT) is designed to realize efficient data dynamics. Finally, the security and performance analysis demonstrates that AIVCI is more secure and efficient.

With growing popularity of cloud storage, the number of users of outsourcing data to cloud servers has increased dramatically. On the one hand, the rapidly increasing volume of data in the cloud is accompanied by a lot of data duplication. On the other hand, the cloud server stores only a unique copy of outsourced data in deduplication cloud storage system and the corruption or missing of the unique copy may bring immeasurable loss. Therefore, the file deduplication and integrity auditing are very important and how to securely and efficiently achieve them simultaneously needs to be settled urgently in academia and industry. In this paper, we propose a confidentiality-preserving deduplication cloud storage with public cloud auditing (CPDA). Firstly, our CPDA scheme achieves secure file deduplication on encrypted file, which supports public integrity auditing for the unique copy in the deduplication cloud storage system. Particularly, our CPDA scheme also realizes secure authentication tag deduplication. Secondly, our CPDA scheme utilizes the convergent encryption and random masking techniques to ensure data confidentiality during the file deduplication and integrity auditing process. Thirdly, our scheme not only supports each data owner to independently launch the integrity auditing of their own files, but also supports cloud server to periodically delegate the third party auditor to concurrently handle multiple auditing tasks to ensure the integrity of the outsourced files. Finally, the security of our scheme is formally proved and its performance is confirmed by numerical analyses and simulation experiments.