Biblio

In recent years, more and more medical institutions have been using electronic medical records (EMRs) to improve service efficiency and reduce storage cost. However, it is difficult for medical institutions with different management methods to share medical data. The medical data of patients is easy to be abused, and there are security risks of privacy data leakage. The above problems seriously impede the sharing of medical data. To solve these problems, we propose a blockchain-based medical data sharing scheme with attribute-based searchable encryption, named BMDS. In BMDS, encrypted EMRs are securely stored in the interplanetary file system (IPFS), while corresponding indexes and other information are stored in a medical consortium blockchain. The proposed BMDS has the features of tamper-proof, privacy preservation, verifiability and secure key management, and there is no single point of failure. The performance evaluation of computational overhead and security analysis show that the proposed BMDS has more comprehensive security features and practicability.

The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large number of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.

With the vast increase in data transmission due to a large number of information collected by devices, data management, and security has been a challenge for organizations. Many data owners (DOs) outsource their data to cloud repositories due to several economic advantages cloud service providers present. However, DOs, after their data are outsourced, do not have complete control of the data, and therefore, external systems are incorporated to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorized access to data but prove to be deficient in providing suitable solutions to the problem. In this article, we propose a secure fine-grain access control system for outsourced data, which supports read and write operations to the data. We make use of an attribute-based encryption (ABE) scheme, which is regarded as a suitable scheme to achieve access control for security and privacy (confidentiality) of outsourced data. This article considers different categories of data users, and make provisions for distinct access roles and permissible actions on the outsourced data with dynamic and efficient policy updates to the corresponding ciphertext in cloud repositories. We adopt blockchain technologies to enhance traceability and visibility to enable control over outsourced data by a DO. The security analysis presented demonstrates that the security properties of the system are not compromised. Results based on extensive experiments illustrate the efficiency and scalability of our system.

Monitoring kernel object modification of virtual machine is widely used by virtual-machine-introspection-based security monitors to protect virtual machines in cloud computing, such as monitoring dentry objects to intercept file operations, etc. However, most of the current virtual machine monitors, such as KVM and Xen, only support page-level monitoring, because the Intel EPT technology can only monitor page privilege. If the out-of-virtual-machine security tools want to monitor some kernel objects, they need to intercept the operation of the whole memory page. Since there are some other objects stored in the monitored pages, the modification of them will also trigger the monitor. Therefore, page-level memory monitor usually introduces overhead to related kernel services of the target virtual machine. In this paper, we propose a low-overhead kernel object monitoring approach to reduce the overhead caused by page-level monitor. The core idea is to migrate the target kernel objects to a protected memory area and then to monitor the corresponding new memory pages. Since the new pages only contain the kernel objects to be monitored, other kernel objects will not trigger our monitor. Therefore, our monitor will not introduce runtime overhead to the related kernel service. The experimental results show that our system can monitor target kernel objects effectively only with very low overhead.

To build a secure communications software, Vulnerability Prediction Models (VPMs) are used to predict vulnerable software modules in the software system before software security testing. At present many software security metrics have been proposed to design a VPM. In this paper, we predict vulnerable classes in a software system by establishing the system's weighted software network. The metrics are obtained from the nodes' attributes in the weighted software network. We design and implement a crawler tool to collect all public security vulnerabilities in Mozilla Firefox. Based on these data, the prediction model is trained and tested. The results show that the VPM based on weighted software network has a good performance in accuracy, precision, and recall. Compared to other studies, it shows that the performance of prediction has been improved greatly in Pr and Re.

Application repackaging is a severe threat to Android users and the market. Existing countermeasures mostly detect repackaging based on app similarity measurement and rely on a central party to perform detection, which is unscalable and imprecise. We instead consider building the detection capability into apps, such that user devices are made use of to detect repackaging in a decentralized fashion. The main challenge is how to protect repackaging detection code from attacks. We propose a creative use of logic bombs, which are regularly used in malware, to conquer the challenge. A novel bomb structure is invented and used: the trigger conditions are constructed to exploit the differences between the attacker and users, such that a bomb that lies dormant on the attacker side will be activated on one of the user devices, while the repackaging detection code, which is packed as the bomb payload, is kept inactive until the trigger conditions are satisfied. Moreover, the repackaging detection code is woven into the original app code and gets encrypted; thus, attacks by modifying or deleting suspicious code will corrupt the app itself. We have implemented a prototype, named BombDroid, that builds the repackaging detection into apps through bytecode instrumentation, and the evaluation shows that the technique is effective, efficient, and resilient to various adversary analysis including symbol execution, multi-path exploration, and program slicing.

Poison message failure is a mechanism that has been responsible for large scale failures in both telecommunications and IP networks. The poison message failure can propagate in the network and cause an unstable network. We apply a machine learning, data mining technique in the network fault management area. We use the k-nearest neighbor method to identity the poison message failure. We also propose a "probabilistic" k-nearest neighbor method which outputs a probability distribution about the poison message. Through extensive simulations, we show that the k-nearest neighbor method is very effective in identifying the responsible message type.