In the Big Data Era, outsourcing computation has become increasingly significant as it supplies computation resources for clients with limited resources. However, there are still many security challenges such as payment fairness, privacy protection, and verification. In this paper, we propose a secure publicly verifiable outsourcing computation scheme for the large-scale matrix QR decomposition. In the proposed scheme, client can pay for outsourcing services through blockchain-based payment system which achieves the payment fairness. Moreover, to protect privacy, both permutation matrix and block diagonal matrix are applied in encryption process. Meanwhile, to achieve the public verification, the computational complexity is reduced by using the matrix digest technology. It is worth mentioning that our scheme is provable and secure under the co-CDH assumption.

Due to the advantages of larger content and error correction capability, quick response (QR) code is commonly used as a tagging technology for the Internet of Things (IoT) recently. However, the cover message of QR code can be easily decoded by a QR code reader, which causes the security and privacy of the cover message to raise the important issues. In this paper we present a new secret hiding scheme based on QR code. The proposed scheme has low computational complexity and is suitable for low-power devices in IoT systems because of utilizing the error correction property of QR code to hide secret information. The proposed scheme hides the secret information without changing the cover message of QR code and the user can get the cover message by using a general scanner, which contributes to reducing attacker’s curiosity. The hidden secret information can be read by a special scanner with the help of the user key. One thing which is better than other known schemes is that the proposed scheme can resist contrast analysis attack. In addition, experimental results show the proposed scheme has feasibility, low computational complexity, and high hiding payload.

With the progress of cloud computing, many users hope in time to upload their data into cloud for sharing. For sensitive data, the owner must encrypt it before sending it to cloud server. The state of the art method for fine-grained access control on encrypted data is attribute-based encryption (ABE). Though ABE is believed to be an outstanding technique for secure data sharing, it has many efficiency drawbacks. For example, its ciphertext size and decryption time increase linearly with the number of attributes used during encryption and decryption, and it is hard to revoke a user’s access ability. In this paper, we propose a method to outsource the decryption of ABE via public cloud computing, and to control a user’s decryption capacity through a bounded revocation technique. The public cloud server can transform any user’s ABE ciphertexts into short ElGamal-type ciphertexts via the user’s public transformation key. The transformed ciphertexts are actually ElGamal-type identity-based ciphertexts obtained through a bounded-collision identity-based encryption scheme, and the decryption time only requires one exponentiation. Our scheme can revoke user’s decryption ability. But it limits to scenario with bounded number of revocation.

The rapid advancements in the Internet of Things (IoT) and cloud computing technologies have significantly promoted the collection and sharing of various data. In order to reduce the communication cost and the storage overhead, it is necessary to exploit data deduplication mechanisms. However, existing data deduplication technologies still suffer security and efficiency drawbacks. In this paper, we propose two secure data deduplication schemes based on Rabin fingerprinting over wireless sensing data in cloud computing. The first scheme is based on deterministic tags and the other one adopts random tags. The proposed schemes realize data deduplication before the data is outsourced to the cloud storage server, and hence both the communication cost and the computation cost are reduced. In particular, variable-size block-level deduplication is enabled based on the technique of Rabin fingerprinting which generates data blocks based on the content of the data. Before outsourcing data to the cloud, users encrypt the data based on convergent encryption technologies, which protects the data from being accessed by unauthorized users. Our security analysis shows that the proposed schemes are secure against offline brute-force dictionary attacks. In addition, the random tag makes the second scheme more reliable. Extensive experimental results indicate that the proposed data deduplication schemes are efficient in terms of the deduplication rate, the system operation time, and the tag generation time.