When users upload their private data to the cloud, they lose control of the data stored in the cloud server. If the cloud system cannot provide an effective security mechanism to protect the data, the consequent data leakage issue will hinder the development of cloud computing. Conventional access control and encryption technologies cannot effectively control the propagation of tenant private data in the system. The mandatory one-way information flow control model is limited by the complexity of the cloud environment, and it is difficult to effectively protect private data stored in the cloud. To solve the above problems, this article proposes a tenant-led ciphertext information flow control method for cloud virtual machines. Through the design of a decentralized information flow control security policy, a secret-domain key management scheme, and a multi-ID-based threshold encryption scheme, the information flow control strategies of taint infection, secret-level reduction, and ability propagation are realized in a ciphertext form, which can effectively prevent malicious users inside and outside the system from illegally reading private data. The feasibility of this method is verified by a security proof and an experiment.

Summary: Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi‐angle. But, due to the impact of the COVID‐19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H‐channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance.

Mobile operating systems such as Android are facing serious security risk. First, they have a large number of users and store a large number of users’ private data, which have become major targets of network attack; second, their openness leads to high security risks; third, their coarse-grained static permission control mechanism leads to a large number of privacy leaks. Recent decentralized information flow control (DIFC) operating systems such as Asbestos, HiStar, and Flume dynamically adjust the label of each process. Asbestos contains inherent covert channels due to this implicit label adjustment. The others close these covert channels through the use of explicit label change, but this impedes communication and increases performance overhead. We present an enhanced implicit label change model (EILCM) for mobile operating systems that can close the known covert channel in these models with implicit label change and supports dynamic constraints on tags for separation of duty. We also formally analyze the reasons why EILCM can close the known covert channels and prove that abstract EILCM systems have the security property of noninterference with declassification by virtue of the model checker tool FDR. We also prove that the problem of EILCM policy verification is NP-complete and propose a backtrack-based search algorithm to solve the problem. Experiments are presented to show that the algorithm is effective.