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In response to the continuous sophistication of cyber threat actors, it is imperative to make the best use of cyber threat intelligence converted from structured or semi-structured data and Named Entity Recognition (NER) techniques that contribute to extracting critical cyber threat intelligence. To promote the NER research in Cyber Threat Intelligence (CTI) domain, we provide a Large Dataset for NER in Cyber Threat Intelligence (LDNCTI). On the LDNCTI corpus, we investigated the feasibility of mainstream transformer-based models in CTI domain. To settle the problem of unbalanced label distribution, we introduce a transformer-based model with a Triplet Loss based on metric learning and Sorted Gradient harmonizing mechanism (TSGL). Our experimental results show that the LDNCTI well represents critical threat intelligence and that our transformer-based model with the new loss function outperforms previous schemes on the Dataset for NER in Threat Intelligence (DNRTI) and the dataset for NER in Advanced Persistent Threats (APTNER). [ABSTRACT FROM AUTHOR]

Ordinary digital signatures have many deficiencies in effectively protecting users' privacy, and signature repudiation and tracking occur from time to time. In order to solve the above problems, a partial blind signature scheme based on the SM9 algorithm was proposed. Partially means that the signature message contains the user's message and other related information, so it can effectively resist signature repudiation and tracking, and introduce a trusted third party to prevent malicious users from tampering with information. Through experimental results and theoretical analysis, the security and efficiency of the proposed algorithm are proved. [ABSTRACT FROM AUTHOR]

The rapid growth of communication networking, ubiquitous sensing, and signal processing, has promoted the development of the Internet of Things (IoT). However, the IoT is essentially dynamic and has no clearly defined network boundary, unauthorized access and data leakage may be much easier. Attribute-based access control (ABAC) can solve the problem of fine-grained access control and large-scale user dynamic expansion in complex information systems, and provides an ideal access control solution for an open network environment, which is more suitable for the dynamic access environment of IoT. However, the dynamic nature of IoT brings new challenges to access control. On the one hand, as new devices and services are continuously deployed, administrators need to manually formulate new rules, which is time-consuming and error-prone. On the other hand, as the IoT environment is continuously changing, the access policy easily becomes unsuitable for the current environment. In order to solve the above two problems, we propose a new scheme named Policy Maintenance-based machine learning (PMML), which includes two modules named Policy Generalization (PG) and Policy Evaluation (PE). After the access control model is deployed, automated PG and PE are carried out to maintain the rule set. In the PG module, we define a novel measure, resource similarity, and integrate it into policy mining so that policies could generalize among related resources. In the PE module, we introduce a quantitative method to assess rules and prune rules of low-quality. We conduct our experiments on real-world enterprise access logs from Amazon, and thoroughly analyzed the effects of different hyper-parameters on the experimental results. The experimental results have qualitatively and quantitatively shown the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data, data privacy protection has become a valuable topic in the field of data publication. Unfortunately, traditional methods of privacy protection, k-anonymity, and its extensions are not absolutely secure as an adversary with background knowledge can determine the owner of a record. The emergence of differential privacy provides a reasonable alternative for privacy security, but the existing solutions ignore the correlation between sensitive attributes and other attributes. In this paper, we propose a new differential privacy scheme based on quasi-identifier classification for big data publication (DP-QIC). It is a new data publishing scheme based on the obfuscation of attribute correlation. We innovatively present quasi-identifier classification based on sensitive attributes and the privacy ratio for evaluating the data set vulnerability. DP-QIC achieves data privacy-protecting through four steps: data collection, grouping and shuffling, generalization, merging, and noise adding, which retains the overall statistical characteristics of the data set. Moreover, the exponential mechanism and the Laplace mechanism are integrated to ensure higher flexibility and a stronger level of privacy protection, so DP-QIC can be used for privacy processing of different data groups in future development. Finally, we have compared the performance of our scheme with the other two famous schemes in the industry. Experimental results demonstrate that DP-QIC has obvious advantages in data utility, privacy protection, and processing efficiency. [ABSTRACT FROM AUTHOR]

Background: There is no consensus on the optimal anesthesia method for intertrochanteric fracture surgeries in elderly patients. Our study aimed to compare the hemodynamics and perioperative outcomes of general anesthesia with endotracheal intubation, combined spinal-epidural anesthesia, and general anesthesia with laryngeal mask airway (LMA) and nerve block for intertrochanteric fracture surgeries in elderly patients. Methods: This is a retrospective study of 75 patients aged > 60 years scheduled for intertrochanteric fracture surgeries with general anesthesia with intubation (n = 25), combined spinal-epidural anesthesia (n = 25), and general anesthesia with LMA and nerve block (n = 25). The intraoperative hemodynamics were recorded, and the maximum variation rate was calculated. Postoperative analgesic effect was evaluated using the visual analog scale (VAS). Postoperative cognitive status was assessed using the Mini-Mental State Exam (MMSE). Results: The maximum variation rate of intraoperative heart rate, systolic blood pressure, diastolic blood pressure differed significantly between the three groups (general anesthesia with intubation > combined spinal-epidural anesthesia > general anesthesia with LMA and nerve block). The VAS scores postoperative 2 h, 4 h, 6 h, and 8 h also differed significantly between the three groups (general anesthesia with intubation > combined spinal-epidural anesthesia > general anesthesia with LMA and nerve block). The VAS scores postoperative 24 h were significantly lower in the general anesthesia with LMA/nerve block group than the general anesthesia with intubation group and the combined spinal-epidural anesthesia group. The MMSE scores postoperative 15 min and 45 min differed significantly between the three groups (general anesthesia with intubation < combined spinal-epidural anesthesia < general anesthesia with LMA and nerve block). The MMSE scores postoperative 120 min in the general anesthesia with intubation group were the lowest among the three groups. There was no significant difference in the incidence of respiratory infection postoperative 24 h, 48 h, and 72 h between the three groups. Conclusion: Compared to general anesthesia with intubation and combined spinal-epidural anesthesia, general anesthesia with LMA and nerve block had better postoperative analgesic effect and less disturbances on intraoperative hemodynamics and postoperative cognition for elderly patients undergoing intertrochanteric fracture surgeries. [ABSTRACT FROM AUTHOR]

Cloud computing provides a wide platform for information sharing. Users can access data and retrieve service easily and quickly. Generally, the data in cloud are transferred with encrypted form to protect the information. As an important technology of cloud security, access control should take account of multi-factor and ciphertext to satisfy the complex requirement for cloud data protection. We propose a proxy re-encryption (PRE) based multi-factor access control (PMAC) for cipher text in the above background. The PMAC adapts to the privacy and the protection of data confidently. We explain the motivation and some assumptions of PMAC at first. Then we define system model and algorithm. The system model and algorithm show how to create the data with corresponding accessing policy and how to grant and revoke the permission. [ABSTRACT FROM AUTHOR]

This paper presents an action-based fine-grained access control mechanism for structured documents. Firstly, we define a describing model for structured documents and analyze the application scenarios. The describing model could support the permission management on chapters, pages, sections, words, and pictures of structured documents. Secondly, based on the action-based access control (ABAC) model, we propose a fine-grained control protocol for structured documents by introducing temporal state and environmental state. The protocol covering different stages from document creation, to permission specification and usage control are given by using the Z-notation. Finally, we give the implementation of our mechanism and make the comparisons between the existing methods and our mechanism. The result shows that our mechanism could provide the better solution of fine-grained access control for structured documents in complicated networks. Moreover, it is more flexible and practical. [ABSTRACT FROM AUTHOR]