With the increasing penetration of the Internet of things (IoT) into people's lives, the limitations of traditional medical systems are emerging. First, the typical way of handling sensitive information can easily lead to privacy disclosure. Second, the medical system is relatively isolated. It is difficult for one medical system to share data with another, and the scope of users' activities is limited within the system boundary. To solve these two problems, we propose a new privacy-preserving medical data-sharing scheme by introducing the authorization mechanism and attribute-based encryption (ABE) based on blockchain, which breaks system boundaries and realizes data sharing among several medical institutions. ABE is used to realize scalable access control. In addition, doctors can share their knowledge to diagnose users by introducing many-to-many matching, which means that patients' health data can be represented by multiple keywords and doctors' expertise can be represented by multiple interests. We provide the correctness and security analysis of our scheme and implement a prototype tool on Ethereum. The experimental results show that our scheme solves the contradiction between the privacy preservation of medical data and the necessity of data sharing.

Testing a program to confirm whether it consistently implements its requirements specification is a necessary but time-consuming activity in software development. Automatic testing based on specifications can significantly alleviate the workload and cost, but faces a challenge of how to ensure that both the user's concerns in the specification and possible execution paths in the program are all covered. In this paper, we describe a new method, called "Vibration-Method" or simply "V-Method", for automatic generation of test cases and test oracle from model-based formal specifications, aiming to address this challenge. The proposed method is suitable for testing information systems in which rich data types are used. Supporting the principle of "divide and conquer", the method provides a specific technique for generating test cases based on functional scenarios defined in the specification, test case generation criteria, automatic test case generation algorithms, and a well-defined mechanism for deriving test oracle. We elaborate on the method by discussing how initial test cases can be automatically generated, how additional necessary test cases are produced using the "vibration" technique, and how a test oracle can be automatically derived for a group of test cases. We also describe a controlled experiment to evaluate the effectiveness of the method and discuss the important issues in relation to the performance and applicability of the method.

With the growing popularity of Android devices, Android malware is seriously threatening the safety of users. Although such threats can be detected by deep learning as a service (DLaaS), deep neural networks as the weakest part of DLaaS are often deceived by the adversarial samples elaborated by attackers. In this paper, we propose a new semi-black-box attack framework called one-feature-each-iteration (OFEI) to craft Android adversarial samples. This framework modifies as few features as possible and requires less classifier information to fool the classifier. We conduct a controlled experiment to evaluate our OFEI framework by comparing it with the benchmark methods JSMF, GenAttack and pointwise attack. The experimental results show that our OFEI has a higher misclassification rate of 98.25%. Furthermore, OFEI can extend the traditional white-box attack methods in the image field, such as fast gradient sign method (FGSM) and DeepFool, to craft adversarial samples for Android. Finally, to enhance the security of DLaaS, we use two uncertainties of the Bayesian neural network to construct the combined uncertainty, which is used to detect adversarial samples and achieves a high detection rate of 99.28%.

While promoting the intelligence in industrial production, Industry 4.0 has also caused privacy leaks concurrently. As a possible solution, the existing personalized federated learning relies too much on a good global model to fine-tune or limit local drift, which lacks intelligent cognitive ability. When faced with heterogeneous data or poisoning attacks, even a few low-quality local models will affect the whole federation effect. In this article, we design a cognitive personalized federated learning (CPFL) mechanism for Industry 4.0, which can selectively improve the collaboration capabilities between more relevant devices. We use the parameters in the local training process as the cognitive basis and calculate Earth mover’s distance to quantify the differences between different models. When the gradient distribution is closer, the local data are more similar. By adaptively adjusting the weight distribution during the aggregation process, self-learning and cooperative learning are balanced, and the interference of heterogeneous data on the federated training process is reduced. Therefore, the global model can better fit most heterogeneous industrial data and achieve personalization. Comparative experimental results show that our proposed CPFL mechanism can increase the average accuracy of personalized models by 5%–10% in non independent and identically distributed situations, and it has certain effects against poisoning attacks and noise interference.

The coronavirus disease 2019 (COVID-19) rapidly spread around the world, and resulted in a global pandemic. Applying artificial intelligence to COVID-19 research can produce very exciting results. However, most research has focused on applying AI techniques in the study of COVID-19, but has ignored the security and reliability of AI systems. In this paper, we explore adversarial attacks on a deep learning system based on COVID-19 CT images with the aim of helping to address this problem. Firstly, we built a deep learning system that could identify COVID-19 CT images and non-COVID-19 CT images with an average accuracy of 76.27%. Secondly, we attacked the pretrained model with an adversarial attack algorithm, i.e., FGSM, to cause the COVID-19 deep learning system to misclassify the CT images, and the classification accuracy of non-COVID-19 CT images dropped from 80% to 0%. Finally, in response to this attack, we proposed how a more secure and reliable deep learning model based on COVID-19 medical images could be built. This research is based on a COVID-19 CT image recognition system, which studies the security of a COVID-19 CT image-based deep learning system. We hope to draw more researchers’ attention to the security and reliability of medical deep learning systems.