Public key encryption with equality test (PKEET) has been widely adopted in applications such as private health record management, secure outsourced data processing, and email filtering, owing to its ability to test equality on ciphertexts encrypted under different public keys. However, existing PKEET schemes often fall short in specialized settings, such as case-insensitive matching. Moreover, their security guarantees remain inadequate. In particular, many existing schemes are vulnerable to offline message recovery attacks (OMRA), which present a significant security challenge to PKEET. Furthermore, the existing IND-CCA security model is incomplete, as it fails to model all potential attacks that an adversary could exploit to execute the OMRA. To address these challenges, we propose a new variant of PKEET, termed public key encryption with case-insensitive fuzzy equality test (PKE-CIFET). To analyze security, we propose a unified security model that more closely aligns with IND-CCA security than previous works. This model includes more comprehensive oracles and considers adversaries launching the OMRA through multi-hop testing. Based on this model, we provide a comprehensive and rigorous security proof. Furthermore, experimental results demonstrate that the proposed PKE-CIFET scheme is efficient in terms of computational cost.

It is widely believed that increasing the amount of training data enhances the intelligence of deep learning models, which in turn heightens dependence on external datasets. However, these datasets are susceptible to adversarial poisoning, allowing attackers to insert backdoors that trigger misclassifications. Although various defense strategies have been suggested, training-phase defenses (TPDs) appear most promising, as they can significantly lower the attack success rate (ASR) without greatly impacting model performance. Nevertheless, creating TPDs that achieve both high accuracy and low ASR is challenging due to two main issues: 1) Many solutions require additional clean samples that match the distribution of the poisoned dataset, which is not always practical in real-world scenarios; 2) most existing solutions have high computational costs, sometimes requiring five to ten times the expense of standard training, which severely limits their practical use. To tackle these challenges, we introduce Confidence Consistency Detection (CCD), an efficient and lightweight training-phase backdoor detection method. CCD is particularly advantageous in situations where clean data is scarce or unavailable, as it completely eliminates the need for external clean samples. Moreover, CCD significantly reduces computational costs to just 25% to 50% of existing solutions (1.7 times the standard training time), providing a notable improvement over current TPD methods. The core innovation of CCD lies in its ability to utilize the high confidence shown by backdoor samples during the early stages of model training for precise detection. Specifically, we initially train a model on a poisoned dataset for a few epochs, followed by intra-class loss fine-tuning to increase sensitivity to poisoned samples. We then create preliminary sets of poisoned and clean samples by assessing the consistency of confidence variations before and after fine-tuning. These sets guide the model training, enabling the detection of high-confidence poisoned samples. Extensive experiments demonstrate that CCD effectively reduces the attack success rate (ASR) to 1.43%, while having a negligible impact on the model’s clean accuracy. In detecting poisoned samples, CCD achieves a 99% true positive rate (TPR) and a 0.033% false positive rate (FPR), setting a new benchmark in the field.

Wide application of the Internet of Things (IoT) system has been increasingly demanding more hardware facilities for processing various resources including data, information, and knowledge. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. Fog computing enables storage and computing services to perform at the edge of the network to extend cloud computing. However, there are some problems such as restricted computation, limited storage, and expensive network bandwidth in Fog computing applications. It is a challenge to balance the distribution of network resources. We propose a processing optimization mechanism of typed resources with synchronized storage and computation adaptation in Fog computing. In this mechanism, we process typed resources in a wireless‐network‐based three‐tier architecture consisting of Data Graph, Information Graph, and Knowledge Graph. The proposed mechanism aims to minimize processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Simulation results show that the proposed approach improves the ratio of performance over user investment. Meanwhile, conversions between resource types deliver support for dynamically allocating network resources.

Web service is a popular solution to integrate components when building a software system, or to allow communication between a system and third-party users, providing a flexible and reusable mechanism to access its functionalities. Various web service based systems are prevailing in health service provision. We propose a framework of construction, searching and protection of typed health resources in terms of data, information and knowledge through a hierarchy consisting of Data Graph, Information Graph and Knowledge Graph. We use cases to illustrate the mechanism of the framework.

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature.It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed.Natural language questions are the most intuitive way of formulating an information need.People can formulate questions to express their information needs.Natural language questions as a query language present an ideal compromise between keyword and structured querying.Questions can be used to express complex information needs that cannot be expressed as keywords without a significant loss in structure and semantics.Knowledge graph has abundant natural semantics and can contain various and more complete information.Its expression mechanism is closer to natural language.We propose to clarify the expression of knowledge graph as a whole.We use knowledge graph to solve the Five Ws problems respectively which are guided by interrogative words such as who/when, what, how and why.We also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.

Both opportunistic routing and inter-flow network coding are useful mechanisms for improving the performance of wireless networks. Both of them exploit the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks. In this paper, we aim at incorporating interflow network coding into opportunistic routing for further improving the performance of wireless mesh networks (WMNs). The main issue in designing such a scheme is candidate set selection and prioritization based on a proper metric for opportunistic routing. To this end, in this paper, we first present a new metric to determine the prioritization of the forwarders in the set of candidates and then design an Inter-flow Network Coding-based Opportunistic Routing (INCOR) scheme using the defined metric. Our proposed INCOR scheme can integrate the characteristics of inter-flow network coding and opportunistic routing effectively to make full use of the broadcast nature of the wireless medium. We carry out extensive simulations to evaluate the effectiveness of the INCOR method. Our data shows that INCOR outperforms both opportunistic routing and inter-flow network coding schemes.

Wireless Mesh Networks (WMNs) have potential to provide convenient broadband wireless Internet access to mobile users. With the emergence of Software-Defined Networking (SDN) paradigm that separates control plane and data plane, WMNs can be easily deployed and managed. In addition, by exploiting the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks, intra-flow network coding has shown a greater benefit in comparison with traditional routing paradigms in data transmission for WMNs. In this paper, we develop a novel OpenCoding protocol, which combines the SDN technique with intra-flow network coding for WMNs. Our developed protocol can simplify the deployment and management of the network and improve network performance. In OpenCoding, a controller working on the control plane makes routing decisions for mesh routers and the hop-by-hop forwarding function is replaced by network coding functions in data plane. Through a simulation study, we show the effectiveness of the OpenCoding protocol in comparison with existing schemes. Our data shows that OpenCoding outperforms both traditional routing and intra-flow network coding schemes.

Dual-loop PI control strategy for LCL type grid-connected converter is usually used in wind power generation. Serious inrush current will be caused when grid-connected converter runs on the rectified status at the start time if no effective control strategy is taken, so it is necessary to improve the startup control performance and the grid voltage feedforward method is usually used. However, in wind power generation, the grid voltage of the transformer's high voltage side can't be directly sampled. To deal with it, a method is proposed based on the feedforward of filter capacitor branch's voltage. Firstly, the mathematical model of LCL type grid-connected converter, under the synchronous rotating reference frame, is established. And the cause of inrush current at the rectified start time of grid-connected converter is analyzed in detail. Then, the feedforward of capacitor branch's voltage is introduced into the output of current loop, and the theoretical analysis on its effectiveness of inrush current suppression is made. Finally, the simulation analysis based on the 10kW LCL type grid-connected converter and the experiments based on the 10kW direct drive wind power grid-connected converter prototype are carried out. The results verify that this method can effectively suppress startup inrush current and improve dynamic response of the grid current control.

For doubly fed induction generation (DFIG) based wind turbine, large electromotive force will be induced in the rotor circuit under severe grid faults, and it usually far exceeds the DC-bus voltage. In that case, it is necessary to inject transient and negative-sequence components into rotor current reference to fulfill controllable low-voltage ride through (LVRT). On the synchronous reference frame of vector control system, such current references are 50 Hz and 100 Hz AC items, respectively. Thus, the classical proportional-integral (PI) controller cannot accurately track this AC reference. To solve this problem, a feedforward current reference (FFCR) control scheme is proposed to improve the tracking capability of AC reference. Hence, it unearths the characteristics of such reference. The proposed FFCR control scheme will not alter the stability of original current loop. Furthermore, it has strong robustness for frequency fluctuations of grid voltage and DFIG parameters drift. Finally, simulation and experimental results are presented to demonstrate the effectiveness of the proposed control scheme.

Pollution attacks refer to ones where attackers modify and inject corrupted data packets into the wireless network with network coding to disrupt the decoding process. In the context of network coding, the epidemic effect of pollution attacks can degrade network throughput significantly because of the mixing nature of network coding. To address this issue, a number of malicious nodes identification schemes have been developed in the past. However, these schemes have their limitations and cannot effectively deal with pollution attacks. In this paper, we propose a novel light-weight Self-checking Pollution Attackers Identification Scheme (SPAIS), which can identify the pollution attackers effectively and efficiently. Through making full use of the broadcast nature of wireless media and insight that a well-behaved node can monitor its downstream neighboring nodes locally by cooperating with other nodes, SPAIS hierarchically organizes the network as levels such that the nodes in the same level can monitor their downstream level nodes cooperatively. Through the combination of theoretical analysis and extensive simulations, our experimental data demonstrates that SPAIS can more effectively identify pollution attackers with a lower cost in comparison with the existing representative schemes. For example, even if the quality of the network connection is not in good condition and the malicious nodes send only one corrupted packet, the pollution attackers can be identified with a high probability.

Network coding provides a promising alternative to the traditional store-and-forward transmission paradigm. For the system using random linear network coding, the adversary could inject corrupted messages into the networks by compromising the network nodes, which is known as the pollution attack. Corrupted messages injected by the adversary, if undetected, could cause a devastating impact to the network performance. To address this issue, a number of pollution attack defense schemes for network coding have been developed in the recent years. The overhead caused by defensive techniques against pollution attacks should be low especially in wireless networks with limited resources. However, there is lack of a systematical strategy for evaluating those schemes and establishing a foundation for designing attack pollution defense schemes for network coding in wireless networks. Towards this end, we first develop the taxonomy of existing network coding authentication schemes. To fairly compare the effectiveness of those schemes, we conduct theoretical analysis and implement different schemes within the same security level. Our extensive simulation and implementation results validate our findings well. Our research summarizes the state-of-art research development and lay out future directions in this area.

By analyzing the basic principle of Otsu method and its application in image segmentation, and according to the distribution characteristics of the target and background, an improved threshold image segmentation algorithm based on the Otsu method is developed. By narrowing the selection range of threshold and searching the minimum variance ratio, the improved algorithm selects the optimal threshold. Through the compared with the Otsu method and other methods, the results show that the new improved algorithm has these advantages such as high segmentation precision and fast computation speed.