The new paradigm of outsourcing data to the cloud is a double-edged sword. On one side, it frees up data owners from the technical management, and is easier for the data owners to share their data with intended recipients when data are stored in the cloud. On the other side, it brings about new challenges about privacy and security protection. To protect data confidentiality against the honest-but-curious cloud service provider, numerous works have been proposed to support fine-grained data access control. However, till now, no efficient schemes can provide the scenario of fine-grained access control together with the capacity of time-sensitive data publishing. In this paper, by embedding the mechanism of timed-release encryption into CP-ABE (Ciphertext-Policy Attribute-based Encryption), we propose TAFC: a new time and attribute factors combined access control on time-sensitive data stored in cloud. Extensive security and performance analysis shows that our proposed scheme is highly efficient and satisfies the security requirements for time-sensitive data storage in public cloud.

In the above paper, Yanget al. have proposed a multi-authority ciphertext-policy attribute-based encryption-based data access control for cloud storage, in which the authors claimed that the mechanism in dealing with attribute revocation could achieve both forward security and backward security. Unfortunately, our further analysis and investigation show that their work adopts a bidirectional re-encryption method in ciphertext updating, so a security vulnerability appears. Our proposed attack method demonstrates that a revoked user can still decrypt new ciphertexts that are claimed to require the new-version secret keys to decrypt.

In this paper, we present a new method for fuzzy multiple attributes group decision-making based on the proposed ranking method of trapezoidal interval type-2 fuzzy sets and the TOPSIS method. Firstly, we present a new method for ranking trapezoidal interval type-2 fuzzy sets. Secondly, we construct the decision matrix and the average decision matrix, respectively. Thirdly, we construct the weighting decision matrix and average weighting decision matrix, respectively. Fourthly, we construct the ranking decision matrix by calculating the ranking values of trapezoidal interval type-2 fuzzy sets in the average decision matrix. Then, we get the absolute positive ideal solution and the absolute negative ideal solution with respect to the attributes based on the ranking decision matrix. Then, we calculate the distance between each alternative and the absolute positive ideal solution and calculate the distance between each alternative and the negative ideal solution, respectively. Finally, we calculate the relative degree of closeness of each alternative. The larger the value of the relative degree of closeness of an alternative, the better the preference order of the alternative. The proposed fuzzy multiple attributes group decision-making method can overcome the drawbacks of the existing method.