With recent advances in cloud computing, mobile devices are increasingly being used to record patient physiological parameters, and transfer them to a cloud-based hospital information system, for access control mediation over a variety of stakeholders. In such a cloud-based architecture, the patient must specify an access policy for a group of authorized parties towards its outsourced data. Multiauthority ciphertext-policy attribute-based encryption (CP-ABE) was provided as an innovative cloud-based access control cryptographic primitive to tackle the key escrow issue in a centralized architecture, and boost flexibility through cross-domain attributes management. Existing works, however, still have glaring drawbacks. First, they still rely on a trusted authority to generate and distribute user secret keys. Second, they do not simultaneously provide encryption, decryption, or revocation outsourcing, resulting in high processing and communication cost for both the data sender and the data receiver. Third, they do not support both user and attribute revocation, and the integrity of ciphertext downloaded from the cloud is not always verified at the user end. As a result, this paper exploits the dummy attribute technique and introduces a novel, efficient, and secure multiauthority ciphertext-policy ABE method for mediating access control over medical data, in the mobile cloud. The ciphertext access policy enforcement, partial ciphertext decryption, and both the user and attribute indirect revocation updates are safely outsourced to the cloud server in this study. Theoretical analysis demonstrates that our scheme is efficient and verifiable, and we prove that our construction is secure under the decisional bilinear Diffie-Hellman assumption.

This paper investigates the emergency group decision-making problem based on interval-valued Pythagorean fuzzy language sets (IVPFLSs). The frequent occurrence of emergency events can bring huge economic damage to human beings. To reduce the loss, it is very important to make reasonable emergency decisions effectively and timely. In the emergency decision making (EDM), these problems are few studied, such as high dimension problem, data nonlinearity, and correlation. For EDM problems, the advantage of IVPFLSs is that it can reasonably express the evaluation information given by decision makers (DMs) through both qualitative and quantitative aspects. However, if the dimension and nonlinear relationship of the decision data keep growing, and the traditional decision-making methods will fail. The distance measure between decision data is necessary to calculate in the process of dimensionality reduction, and the current research does not propose the definition of IVPFLSs distance measure. On the basis of this, this paper first uses the attributes and DMs as variables to define the standard Euclidean distance measure between IVPFLSs. For nonlinear features, we construct the interval-valued Pythagorean fuzzy language kernel principal component analysis (IVPFL-KPCA) model to reduce the dimensionality. What is more, we also obtain the reasonable weight vectors of the attribute and DMs from cumulative contribution rate. For low-dimensional decision data, according to the technique for order performance by similarity to ideal solution method, the best emergency plan is selected for the information variables after dimensionality reduction. In sum, the IVPFL-KPCA model not only avoids multicollinearity and nonlinear separability between decision data, but also obtains reasonable weights. It further improves the efficiency of decision-making operations and reduces the difficulty of the algorithm. Finally, an example of earthquake emergency plan is shown to demonstrate the feasibility and practicability of the proposed method. Besides, we also compare it with the existing methods, which proves the effectiveness of the method.

Federated Learning (FL) enables the deployment of distributed machine learning models over the cloud and Edge Devices (EDs) while preserving the privacy of sensitive local data, such as electronic health records. However, despite FL advantages regarding security and flexibility, current constructions still suffer from some limitations. Namely, heavy computation overhead on limited resources EDs, communication overhead in uploading converged local models' parameters to a centralized server for parameters aggregation, and lack of guaranteeing the acquired knowledge preservation in the face of incremental learning over new local data sets. This paper introduces a secure and resource-friendly protocol for parameters aggregation in federated incremental learning and its applications. In this study, the central server relies on a new method for parameters aggregation called orthogonal gradient aggregation. Such a method assumes constant changes of each local data set and allows updating parameters in the orthogonal direction of previous parameters spaces. As a result, our new construction is robust against catastrophic forgetting, maintains the federated neural network accuracy, and is efficient in computation and communication overhead. Moreover, extensive experiments analysis over several significant data sets for incremental learning demonstrates our new protocol's efficiency, efficacy, and flexibility.