Post-Quantum κ\kappa-to-1 Trapdoor Claw-free Functions from Extrapolated Dihedral Cosets

\emph{Noisy trapdoor claw-free function} (NTCF) as a powerful post-quantum cryptographic tool can efficiently constrain actions of untrusted quantum devices. However, the original NTCF is essentially \emph{2-to-1} one-way function (NTCF$^1_2$). In this work, we attempt to further extend the NTCF$^1_2$ to achieve \emph{many-to-one} trapdoor claw-free functions with polynomial bounded preimage size. Specifically, we focus on a significant extrapolation of NTCF$^1_2$ by drawing on extrapolated dihedral cosets, thereby giving a model of NTCF$^1_{\kappa}$ where $\kappa$ is a polynomial integer. Then, we present an efficient construction of NTCF$^1_{\kappa}$ assuming \emph{quantum hardness of the learning with errors (LWE)} problem. We point out that NTCF can be used to bridge the LWE and the dihedral coset problem (DCP). By leveraging NTCF$^1_2$ (resp. NTCF$^1_{\kappa}$), our work reveals a new quantum reduction path from the LWE problem to the DCP (resp. extrapolated DCP). Finally, we demonstrate the NTCF$^1_{\kappa}$ can naturally be reduced to the NTCF$^1_2$, thereby achieving the same application for proving the quantumness.Comment: 34 pages, 7 figure

Identification of a novel functional deletion variant in the 5'-UTR of the DJ-1 gene

<p>Abstract</p> <p>Background</p> <p>DJ-1 forms part of the neuronal cellular defence mechanism against oxidative insults, due to its ability to undergo self-oxidation. Oxidative stress has been implicated in the pathogenesis of central nervous system damage in different neurodegenerative disorders including Alzheimer's disease and Parkinson's disease (PD). Various mutations in the <it>DJ-1 </it>(<it>PARK7</it>) gene have been shown to cause the autosomal recessive form of PD. In the present study South African PD patients were screened for mutations in <it>DJ-1 </it>and we aimed to investigate the functional significance of a novel 16 bp deletion variant identified in one patient.</p> <p>Methods</p> <p>The possible effect of the deletion on promoter activity was investigated using a Dual-Luciferase Reporter assay. The <it>DJ-1 </it>5'-UTR region containing the sequence flanking the 16 bp deletion was cloned into a pGL4.10-Basic luciferase-reporter vector and transfected into HEK293 and BE(2)-M17 neuroblastoma cells. Promoter activity under hydrogen peroxide-induced oxidative stress conditions was also investigated. Computational (<it>in silico</it>) <it>cis</it>-regulatory analysis of <it>DJ-1 </it>promoter sequence was performed using the transcription factor-binding site database, TRANSFAC via the PATCH™ and rVISTA platforms.</p> <p>Results</p> <p>A novel 16 bp deletion variant (g.-6_+10del) was identified in <it>DJ-1 </it>which spans the transcription start site and is situated 93 bp 3' from a Sp1 site. The deletion caused a reduction in luciferase activity of approximately 47% in HEK293 cells and 60% in BE(2)-M17 cells compared to the wild-type (<it>P </it>< 0.0001), indicating the importance of the 16 bp sequence in transcription regulation. The activity of both constructs was up-regulated during oxidative stress. Bioinformatic analysis revealed putative binding sites for three transcription factors AhR, ARNT, HIF-1 within the 16 bp sequence. The frequency of the g.-6_+10del variant was determined to be 0.7% in South African PD patients (2 heterozygotes in 148 individuals).</p> <p>Conclusion</p> <p>This is the first report of a functional <it>DJ-1 </it>promoter variant, which has the potential to influence transcript stability or translation efficiency. Further work is necessary to determine the extent to which the g.-6_+10del variant affects the normal function of the <it>DJ-1 </it>promoter and whether this variant confers a risk for PD.</p

Research and Application of Generative-Adversarial-Network Attacks Defense Method Based on Federated Learning

In recent years, Federated Learning has attracted much attention because it solves the problem of data silos in machine learning to a certain extent. However, many studies have shown that attacks based on Generative Adversarial Networks pose a great threat to Federated Learning. This paper proposes Defense-GAN, a defense method against Generative Adversarial Network attacks under Federated Learning. Under this method, the attacker cannot learn the real image data distribution. Each Federated Learning participant uses SHAP to explain the model and masks the pixel features that have a greater impact on classification and recognition in their respective image data. The experimental results show that while attacking the federated training model using masked images, the attacker cannot always obtain the ground truth of the images. At the same time, this paper also uses CutMix to improve the generalization ability of the model, and the obtained model accuracy is only 1% different from that of the model trained with the original data. The results show that the defense method proposed in this paper can not only resist Generative Adversarial Network attacks in Federated Learning and protect client privacy, but also ensure that the model accuracy of the Federated model will not be greatly affected

Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection

With the rapid development of the network, data fusion becomes an important research hotspot. Large amounts of data need to be preprocessed in data fusion; in practice, the features of datasets can be filtered to reduce the amount of data. The feature selection based on fuzzy rough sets can process a large number of continuous and discrete data to reduce the data dimension, making the selected feature subset highly correlated with the classification but less dependent on other features. In this paper, a new method of fuzzy rough feature selection is proposed which combines the membership function determination method of fuzzy c-means clustering and fuzzy equivalence to the original selection. Different from the existing research, our method takes full advantage of knowledge about the dataset itself and the differences between datasets, which makes the features selected have a higher correlation with the classification, improves the classification accuracy, and reduces the data dimension. Experimental results on the UCI machine learning repository datasets confirmed the performance and effectiveness of our method. Compared to the existing method, smaller subsets of features and an average of 1% higher classification accuracies were achieved

Deep Stacking Network for Intrusion Detection

Preventing network intrusion is the essential requirement of network security. In recent years, people have conducted a lot of research on network intrusion detection systems. However, with the increasing number of advanced threat attacks, traditional intrusion detection mechanisms have defects and it is still indispensable to design a powerful intrusion detection system. This paper researches the NSL-KDD data set and analyzes the latest developments and existing problems in the field of intrusion detection technology. For unbalanced distribution and feature redundancy of the data set used for training, some training samples are under-sampling and feature selection processing. To improve the detection effect, a Deep Stacking Network model is proposed, which combines the classification results of multiple basic classifiers to improve the classification accuracy. In the experiment, we screened and compared the performance of various mainstream classifiers and found that the four models of the decision tree, k-nearest neighbors, deep neural network and random forests have outstanding detection performance and meet the needs of different classification effects. Among them, the classification accuracy of the decision tree reaches 86.1%. The classification effect of the Deeping Stacking Network, a fusion model composed of four classifiers, has been further improved and the accuracy reaches 86.8%. Compared with the intrusion detection system of other research papers, the proposed model effectively improves the detection performance and has made significant improvements in network intrusion detection

Research on Alarm Reduction of Intrusion Detection System Based on Clustering and Whale Optimization Algorithm

With the frequent occurrence of network security events, the intrusion detection system will generate alarm and log records when monitoring the network environment in which a large number of log and alarm records are redundant, which brings great burden to the server storage and security personnel. How to reduce the redundant alarm records in network intrusion detection has always been the focus of researchers. In this paper, we propose a method using the whale optimization algorithm to deal with massive redundant alarms. Based on the alarm hierarchical clustering, we integrate the whale optimization algorithm into the process of generating alarm hierarchical clustering and optimizing the cluster center and put forward two versions of local hierarchical clustering and global hierarchical clustering, respectively. To verify the feasibility of the algorithm, we conducted experiments on the UNSW-NB15 data set; compared with the previous alarm clustering algorithms, the alarm clustering algorithm based on the whale optimization algorithm can generate higher quality clustering in a shorter time. The results show that the proposed algorithm can effectively reduce redundant alarms and reduce the load of IDS and staff

Statistic Experience Based Adaptive One-Shot Detector (EAO) for Camera Sensing System

Object detection in a camera sensing system has been addressed by researchers in the field of image processing. Highly-developed techniques provide researchers with great opportunities to recognize objects by applying different algorithms. This paper proposes an object recognition model, named Statistic Experience-based Adaptive One-shot Detector (EAO), based on convolutional neural network. The proposed model makes use of spectral clustering to make detection dataset, generates prior boxes for object bounding and assigns prior boxes based on multi-resolution. The model is constructed and trained for improving the detection precision and the processing speed. Experiments are conducted on classical images datasets while the results demonstrate the superiority of EAO in terms of effectiveness and efficiency. Working performance of the EAO is verified by comparing it to several state-of-the-art approaches, which makes it a promising method for the development of the camera sensing technique

Attribute-Based Fully Homomorphic Encryption Scheme from Lattices with Short Ciphertext

Attribute-based encryption (ABE) is a good choice for one-to-many communication and fine-grained access control of the encryption data in a cloud environment. Fully homomorphic encryption (FHE) allows cloud servers to make valid operations on encrypted data without decrypting. Attribute-based fully homomorphic encryption (ABFHE) from lattices not only combines the bilateral advantages/facilities of ABE and FHE but also can resist quantum attacks. However, in the most previous ABFHE schemes, the growth of ciphertext size usually depends on the total number of system’s attributes which leads to high communication overhead and long running time of encryption and decryption. In this paper, based on the LWE problem on lattices, we propose an attribute-based fully homomorphic scheme with short ciphertext. More specifically, by classifying the system’s attributes and using the special structure matrix in MP12, we remove the dependency of ciphertext size on system’s attributes ℓ and the ciphertext size is no longer increased with the total number of system’s attributes. In addition, by introducing the function G−1 in the homomorphic operations, we completely rerandomize the error term in the new ciphertext and have a very tight and simple error analysis using sub-Gaussianity. Besides, performance analysis shows that when ℓ=2 and n=284 according to the parameter suggestion given by Micciancio and Dai et al., the size of ciphertext in our scheme is reduced by at least 73.3%, not to mention ℓ>2. The larger the ℓ, the more observable of our scheme. The short ciphertext in our construction can not only reduce the communication overhead but also reduce the running time of encryption and decryption. Finally, our scheme is proved to be secure in the standard model