Blockchain provides new technologies and ideas for the construction of agricultural product traceability system (APTS). However, if data is stored, supervised, and distributed on a multiparty equal blockchain, it will face major security risks, such as data privacy leakage, unauthorized access, and trust issues. How to protect the privacy of shared data has become a key factor restricting the implementation of this technology. We propose a secure and trusted agricultural product traceability system (BCST-APTS), which is supported by blockchain and CP-ABE encryption technology. It can set access control policies through data attributes and encrypt data on the blockchain. This can not only ensure the confidentiality of the data stored in the blockchain, but also set flexible access control policies for the data. In addition, a whole-chain attribute management infrastructure has been constructed, which can provide personalized attribute encryption services. Furthermore, a reencryption scheme based on ciphertext-policy attribute encryption (RE-CP-ABE) is proposed, which can meet the needs of efficient supervision and sharing of ciphertext data. Finally, the system architecture of the BCST-APTS is designed to successfully solve the problems of mutual trust, privacy protection, fine-grained, and personalized access control between all parties.
Internet of drones (IoD) is a network of small drones that leverages IoT infrastructure to deliver real-time data communication services to users. On the one hand, IoD is an excellent choice for a number of military and civilian applications owing to key characteristics like agility, low cost, and ease of deployment; on the other hand, small drones are rarely designed with security and privacy concerns in mind. Intruders can exploit this vulnerability to compromise the security and privacy of IoD networks and harm the information exchange operation. An aggregate signature scheme is the best solution for resolving security and privacy concerns since multiple drones are connected in IoD networks to gather data from a certain zone. However, most aggregate signature schemes proposed in the past for this purpose are either identity-based or relied on certificateless cryptographic methods. Using these methods, a central authority known as a trusted authority (TA) is responsible for generating and distributing secret keys of every user. However, the key escrow problem is formulated as knowing the secret key generated by the TA. These methods are hampered by key distribution issues, which restrict their applicability in a variety of situations. To address these concerns, this paper presents a certificate-based aggregate signature (CBS-AS) scheme based on hyperelliptic curve cryptography (HECC). The proposed scheme has been shown to be both efficient in terms of computation cost and unforgeable while testing its toughness through formal security analysis.
Internet of Things (IoT) device identification is a key step in the management of IoT devices. The devices connected to the network must be controlled by the manager. For this purpose, many schemes are proposed to identify IoT devices, especially the schemes working on the gateway. However, almost all researchers do not pay close attention to the cost. Thus, considering the gateway’s limited storage and computational resources, a new lightweight IoT device identification scheme is proposed. First, the DFI (deep/dynamic flow inspection) technology is utilized to efficiently extract flow-related statistical features based on in-depth studies. Then, combined with symmetric uncertainty and correlation coefficient, we proposed a novel filter feature selection method based on NSGA-III to select effective features for IoT device identification. We evaluate our proposed method by using a real smart home IoT data set and three different ML algorithms. The experimental results showed that our proposed method is lightweight and the feature selection algorithm is also effective, only using 6 features can achieve 99.5% accuracy with a 3-minute time interval.
Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.
Social networks contain a large amount of unstructured data. To ensure the stability of unstructured big data, this study proposes a method for visual dynamic simulation model of unstructured data in social networks. This study uses the Hadoop platform and data visualization technology to establish a univariate linear regression model according to the time correlation between data, estimates and approximates perceptual data, and collects unstructured data of social networks. Then, the unstructured data collected from the original social network are processed, and an adaptive threshold is designed to filter out the influence of noise. The unstructured data of social network after feature analysis are processed to extract its visual features. Finally, this study carries out the Hadoop cluster design, implements data persistence by HDFS, uses MapReduce to extract data clusters for distributed computing, builds a visual dynamic simulation model of unstructured data in social network, and realizes the display of unstructured data in social network. The experimental results show that this method has a good visualization effect on unstructured data in social networks and can effectively improve the stability and efficiency of unstructured data visualization in social networks.
Image saliency object detection can rapidly extract useful information from image scenes and further analyze it. At present, the traditional saliency target detection technology still has the edge of outstanding target that cannot be well preserved. Convolutional neural network (CNN) can extract highly general deep features from the images and effectively express the essential feature information of the images. This paper designs a model which applies CNN in deep saliency object detection tasks. It can efficiently optimize the edges of foreground objects and realize highly efficient image saliency detection through multilayer continuous feature extraction, refinement of layered boundary, and initial saliency feature fusion. The experimental result shows that the proposed method can achieve more robust saliency detection to adjust itself to complex background environment.
With the advent of the era of big data, people’s lives have undergone earth-shaking changes, not only getting rid of the cumbersome traditional data collection but also collecting and sorting information directly from people’s footprints on social networks. This paper explores and analyzes the privacy issues in current social networks and puts forward the protection strategies of users’ privacy data based on data mining algorithms so as to truly ensure that users’ privacy in social networks will not be illegally infringed in the era of big data. The data mining algorithm proposed in this paper can protect the user’s identity from being identified and the user’s private information from being leaked. Using differential privacy protection methods in social networks can effectively protect users’ privacy information in data publishing and data mining. Therefore, it is of great significance to study data publishing, data mining methods based on differential privacy protection, and their application in social networks.
With the prevalence of online social networks, the potential threat of misinformation has greatly enhanced. Therefore, it is significant to study how to effectively control the spread of misinformation. Publishing the truth to the public is the most effective approach to controlling the spread of misinformation. Knowledge popularization and expert education are two complementary ways to achieve that. It has been proven that if these two ways can be combined to speed up the release of the truth, the impact caused by the spread of misinformation will be dramatically reduced. However, how to reasonably allocate resources to these two ways so as to achieve a better result at a lower cost is still an open challenge. This paper provides a theoretical guidance for designing an effective collaborative resource allocation strategy. First, a novel individual-level misinformation spread model is proposed. It well characterizes the collaborative effect of the two truth-publishing ways on the containment of misinformation spread. On this basis, the expected cost of an arbitrary collaborative strategy is evaluated. Second, an optimal control problem is formulated to find effective strategies, with the expected cost as the performance index function and with the misinformation spread model as the constraint. Third, in order to solve the optimal control problem, an optimality system that specifies the necessary conditions of an optimal solution is derived. By solving the optimality system, a candidate optimal solution can be obtained. Finally, the effectiveness of the obtained candidate optimal solution is verified by a series of numerical experiments.
The research on the relationship between human resource management activities and performance is an important topic of enterprise human resource management research. There are some errors between the relationship between human resource management activities and performance and the real situation, so it is impossible to accurately predict the performance fluctuation. Therefore, the relationship model between human resource management activities and performance based on the LMBP algorithm is constructed. Using the Levenberg–Marquardt (LM) algorithm and BP (back-propagation) neural network algorithm to establish a new LMBP algorithm, control the convergence of the new algorithm, optimize the accuracy of the algorithm, and then apply the LMBP algorithm to predict the risk of performance fluctuation under human resource management activities of enterprises, the indicators of human resource management activities of enterprises are determined, to complete the mining of enterprise performance data, the grey correlation analysis is combined, and the relationship model between human resource management activities and performance is built. The experimental samples are selected from CSMAR database, and the simulation experiment is designed. Using different algorithms to forecast the fluctuation of enterprise performance, the experimental results show that the LMBP algorithm can more accurately reflect the relationship between enterprise HRM and performance.
For the last two decades, cybercrimes are growing on a daily basis. To track down cybercrimes and radio network crimes, digital forensic for radio networks provides foundations. The data transfer rate for the next-generation wireless networks would be much greater than today’s network in the coming years. The fifth-generation wireless systems are considering bands beyond 6 GHz. The network design of the next-generation wireless systems depends on propagation characteristics, frequency reuse, and bandwidth variation. This article declares the channel’s propagation characteristics of both line of sight (LoS) and non-LOS (NLoS) to construct and detect the path of rays coming from anomalies. The simulations were carried out to investigate the diffraction loss (DL) and frequency drop (FD). Indoor and outdoor measurements were taken with the omnidirectional circular dipole antenna with a transmitting frequency of 28 GHz and 60 GHz to compare the two bands of the 5th generation. Millimeter-wave communication comes with a higher constraint for implementing and deploying higher losses, low diffractions, and low signal penetrations for the mentioned two bands. For outdoor, a MATLAB built-in 3D ray tracing algorithm is used while for an indoor office environment, an in-house algorithmic simulator built using MATLAB is used to analyze the channel characteristics.