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The release of millions of financial documents, which has been known as the 'WikiLeaks' of the financial world (a.k.a. 'Panama Papers'), has dragged global attention in how highly structured means applied by some of the elite to conceal their financial assets. Consequently, significant financial corruption allegations were raised. We concentrate on a somewhat overlooked region, the Middle East and North Africa (MENA) region. This study aims to use social network analytics to study the information contained in these documents. We are checking the major players in the MENA's trends and patterns to determine if it matches the known economic powers. The analysis reveals that while the constructed network enjoys some typical characteristics, many interesting observations and properties are worth discussing. Specifically, using the extracted network consisting of 62 987 nodes and 84 692 edges, our social network analysis finding shows that, perhaps surprisingly, the nodes or the social network are not necessarily directly correlated with perceived economic influence. [ABSTRACT FROM AUTHOR]

We consider two finite and disjoint sets of homogeneous robots deployed at the nodes of an infinite grid graph. The grid graph also comprises two finite and disjoint sets of prefixed meeting nodes located over the nodes of the grid. The objective of our study is to design a distributed algorithm that gathers all the robots belonging to the first team at one of the meeting nodes belonging to the first type, and all the robots in the second team must gather at one of the meeting nodes belonging to the second type. The robots can distinguish between the two types of meeting nodes. However, a robot cannot identify its team members. This paper assumes the strongest adversarial model, namely the asynchronous scheduler. We have characterized all the initial configurations for which the gathering problem is unsolvable. For the remaining initial configurations, the paper proposes a distributed gathering algorithm. Assuming the robots are capable of global-weak multiplicity detection , the proposed algorithm solves the problem within a finite time period. The algorithm runs in |$\Theta (dn)$| moves and |$O(dn)$| epochs, where |$d$| is the diameter of the minimum enclosing rectangle of all the robots and meeting nodes in the initial configuration, and |$n$| is the total number of robots in the system. [ABSTRACT FROM AUTHOR]

Ridesharing is an effective approach to alleviate traffic congestion. In most existing works, drivers and passengers are assigned prices without considering the constraints of desired benefits. This paper investigates ridesharing by formulating a matching and pricing problem to maximize the total payoff of drivers, with the constraints of desired benefit and quality of experience. An efficient algorithm is proposed to solve the formulated problem based on coalitional double auction. Secondary pricing based strategy and sacrificed minimum bid based strategy are proposed to support the algorithm. This paper also proves that the proposed algorithm can achieve a Nash-stable coalition partition in finite steps, and the proposed two strategies guarantee truthfulness, individually rational and budget balance. Extensive simulation results on the real-world dataset of taxi trajectory in Beijing city show that the proposed algorithm outperforms the existing ones, in terms of average total payoff of drivers while meeting the benefits of passengers. [ABSTRACT FROM AUTHOR]

The synchronous stream ciphers RCR-64 and RCR-32 designed by Sekar, Paul and Preneel are strengthened variants of the ciphers TPy and TPypy (designed by Biham and Seberry), respectively. The RCR ciphers have remained unbroken since they were published in 2007. In this paper, we present arguments that not only support the designers' security claims but suggest, in general, that the ciphers are secure against several classes of cryptanalytic attacks. We find that the ciphers are best used with 256-bit keys and 384-bit IVs. We also suggest ways to protect software implementations of the RCR ciphers against (cache-)timing and processor flag attacks. Our performance evaluation suggests that the protected implementation of the RCR-64 encrypts long messages at speeds comparable to some of the fastest stream ciphers available today. Consequently, we find that the RCR ciphers may be well suited for PC-based applications in general and streaming audio / video applications in particular. This is the first paper to present a detailed study on the security and performance of the RCR ciphers. [ABSTRACT FROM AUTHOR]

As code is now an inextricable part of science it should be supported by competent Software Engineering, analogously to statistical claims being properly supported by competent statistics. If and when code avoids adequate scrutiny, science becomes unreliable and unverifiable because results — text, data, graphs, images, etc — depend on untrustworthy code. Currently, scientists rarely assure the quality of the code they rely on, and rarely make it accessible for scrutiny. Even when available, scientists rarely provide adequate documentation to understand or use it reliably. This paper proposes and justifies ways to improve science using code: 1.   Professional Software Engineers can help, particularly in critical fields such as public health, climate change and energy. 2.   'Software Engineering Boards,' analogous to Ethics or Institutional Review Boards, should be instigated and used. 3.   The Reproducible Analytic Pipeline (RAP) methodology can be generalized to cover code and Software Engineering methodologies,        in a generalization this paper introduces called RAP +. RAP + (or comparable interventions) could be supported and or even required       in journal, conference and funding body policies. The paper's Supplemental Material provides a summary of Software Engineering best practice relevant to scientific research, including further suggestions for RAP + workflows. 'Science is what we understand well enough to explain to a computer.' Donald E. Knuth in |$A=B$| [ 1 ] 'I have to write to discover what I am doing.' Flannery O'Connor, quoted in Write for your life [ 2 ] 'Criticism is the mother of methodology.' Robert P. Abelson in Statistics as Principled Argument [ 3 ] 'From its earliest times, science has operated by being open and transparent about methods and evidence, regardless of which technology has been in vogue.' Editorial in Nature [ 4 ] [ABSTRACT FROM AUTHOR]

With the development of NAND flash, the storage density has become larger and larger, but the reliability has become lower and lower. To address this problem, stronger error correction codes, low-density parity-check (LDPC) codes, are widely used in flash memory. However, the direct use of LDPC codes for error correction has a great impact on the read and write performance of SSDs. Given this, this paper proposes a new mentality that combines replica and LDPC decoding methods to improve the read performance of solid state drives (SSDs). Considering the characteristics of the quantization level of LDPC soft-decision decoding, this paper proposes the IDSD (LDPC soft-decision decoding and duplication method) and IDHSD (a method combining duplication and LDPC hybrid decoding)schemes. These schemes are mainly for hot data. When reading the data, the IDSD scheme needs to first judge whether it is soft-decision decoding. Then, according to the number of quantization levels recorded, it needs to select whether to use duplication or soft-decision decoding to correct the error data. The IDHSD scheme does not need to judge the soft-decision decoding but directly uses the duplication after the hard-decision decoding fails. Both schemes reduce the average response time to a certain extent and optimize the read performance of the SSD. The experimental results show that compared with the Native Scheme, the average response delay time of the IDSD scheme decreases by 5–30% and the IDHSD scheme slashes by 5–38%. Compared with the Traditional Refresh Scheme, the average response delay time of the IDSD scheme cuts down by 1–30% and the IDHSD scheme lowers by 1–29%. [ABSTRACT FROM AUTHOR]

The brain is regarded as the central part of the human body and has a very complicated structure. The abnormal growth of tissue inside the brain is called a brain tumour. Tumour detection at an early stage is the most difficult task in the discipline of health. In this review article, the authors have deeply analysed and reviewed the brain tumour detection mechanisms which include manual, semi- and fully automated techniques. Today, fully automated mechanisms apply deep learning (DL) methods for tumour detection in brain magnetic resonance images (MRIs). This paper deals with previously published research articles relevant to various brain tumour detection techniques. Review of various types of tumours, MRI modalities, datasets, filters, segmentation methods and DL techniques like long short-term memory, gated recurrent unit network, convolution neural network, auto encoder, deep belief network, recurrent neural network, generative adverse network and deep stacking networks have been included in this paper. It has been observed from the analysis that the use of DL techniques in the detection of brain tumours improves accuracy. Finally, this paper reveals research gaps, limitations of existing methods, challenges in tumour detection and contributions of the proposed article. [ABSTRACT FROM AUTHOR]

Cloud data center serves tremendous workload demand due to the ever-increasing usage of internet services. Scheduling of these workloads over the physical servers is the combinatorial problem that resembles to an NP-complete problem. Furthermore, the workload is dynamic and changes at each scheduling interval result in high power consumption and Service Level Agreement (SLA) violation. Virtual Machine (VM) migrations provide the opportunity to balance this dynamic workload. However, it results in additional power consumption and performance loss. Therefore, this paper aims to find the optimal VM scheduling with minimum VM migration to minimize power consumption and ensure the SLA. This paper proposes a Residual Optimum Power Efficiency (ROPE) aware Improved Clonal Selection Algorithm (ICSA) for dynamic VM scheduling. ICSA-ROPE algorithm finds optimal VM schedules at each scheduling interval guided by two optimization functions Total Datacenter Residual Optimum Power-Efficiency (TDCROPE) and VM Migration Cost (VMC). TDCROPE ensures that servers operate at optimum power efficiency as they consume less power and are less prone to SLA violation, while VMC ensures to find optimal VM schedule with minimum VM migrations. The proposed approach is implemented on a CloudSim simulator, and results show that the ICSA-ROPE is 95.54 %,90.34% and 88.49% more significant in terms of performance efficiency than the LrMmt, DthMf and VMS-MCSA. [ABSTRACT FROM AUTHOR]

Whirlpool is a hash function that has been standardized by ISO/IEC. In this paper, we develop a new type of distinguishing property for its underlying permutation |$ W $|⁠. Division property proposed by Todo at EUROCRYPT 2015 was initially used in the integral cryptanalysis of symmetric-key algorithms. This work for the first time utilizes the MILP method to search for the integral distinguishers of |$ W $| in both the forward and backward directions while concentrating on word-based division property. Under the known-key model, the fact that the permutation used in the hash function does not depend on any secret parameters allows the previous properties to be exploited from the middle, i.e. from an intermediate internal state. Therefore, we apply the inside-out strategy which is the essential step in the zero-sum property to connect the trails in opposite directions. Consequently, we obtain new distinguishers up to full rounds for the |$ W $|⁠. To further reduce the complexity of the integral distinguishers, we add one round in the middle with the help of subspace trails. Finally, we succeed in extending the length and improving the complexity of the integral distinguishers. To the best of our knowledge, all the results in this paper are competitive with the previous work in both computational cost and memory complexity. It is worth mentioning that the methods presented in this paper are applicable to a broad class of hash functions. [ABSTRACT FROM AUTHOR]

Edge connectivity is an important parameter for the reliability of the inter-connection network. A graph |$G$| is strong Menger edge-connected (⁠|$SM$| - |$\lambda $| for short) if there exist min |$\{\deg _{G}(u),\deg _{G}(v)\}$| edge-disjoint paths between any pair of vertices |$u$| and |$v$| of |$G$|⁠. The conditional edge-fault-tolerance strong Menger edge connectivity of |$G$|⁠ , denoted by |$sm_{\lambda }^{r}(G)$|⁠ , is the maximum integer |$m$| such that |$G-F$| remains |$SM$| - |$\lambda $| for any edge set |$F$| with |$|F|\leq m$| and |$\delta (G-F)\geq r$|⁠ , where |$\delta (G-F)\geq r$| is the minimum degree of |$G-F$|⁠. Most of the previous papers discussed |$sm_{\lambda }^{r}(G)$| in the case of |$r\leq 2$|⁠. In this paper, we show that |$sm_{\lambda }^{r}(FQ_{n})=2^{r}(n-r+1)-(n+1)$| for |$1\leq r\leq n-2$|⁠ , where |$n\geq 4$|⁠. [ABSTRACT FROM AUTHOR]

This paper studies adversarial attacks on network intrusion detection systems (IDSs) based on deep or machine learning algorithms. Adversarial attacks on network IDSs must maintain the functional logic of the attack flow. To prevent the produced adversarial examples from violating the attack behavior, most solutions define some limited modification actions. The result limits the production of adversarial examples, and the produced adversarial examples are not guaranteed to find the attack packets. This paper proposes the concept of flow containers to model packets in a flow. Then, we propose a generative adversarial network framework with dual adversarial training to train the generator to produce adversarial flow containers. Flow containers can correlate attack packets and feature vectors of attack flows. We test the evasion rate of the produced adversarial examples using 12 deep and machine learning algorithms. For experiments on the CTU42 data set, the proposed adversarial examples have the highest evasion rates among all 12 classifiers, with the highest evasion rate as high as 1.00. For experiments on the CIC-IDS2017 data set, the proposed adversarial examples have the highest evasion rate among the five classifiers, and the highest evasion rate is also up to 1.00. [ABSTRACT FROM AUTHOR]

This paper presents an artificial intelligence deep learning model for an energy-aware task scheduling algorithm based on learning automata (LA) in the Fog Computing (FC) Applications. FC is a distributed computing model that serves as an intermediate layer between the cloud and Internet of Things (IoT) to improve the quality of service. The IoT is the closest model to the wireless sensor network (WSN). One of its important applications is to create a global approach to health care system infrastructure development that reflects recent advances in WSN. The most influential factor in energy consumption is task scheduling. In this paper, the issue of reducing energy consumption is investigated as an important challenge in the fog environment. Also, an algorithm is presented to solve the task scheduling problem based on LA and measure the makespan (MK) and cost parameters. Then, a new artificial neural network deep model is proposed, based on the presented LA task scheduling fog computing algorithm. The proposed neural model can predict the relation among MK, energy and cost parameters versus VM length for the first time. The proposed model results show that all of the desired parameters can be predicted with high precision. [ABSTRACT FROM AUTHOR]

Security faces huge challenges in Internet of Things (IoT) environments. In particular, conventional access control standards and models tend to be less tailored for IoT due to the constrained nature of smart objects. Usually, a powerful third party is used to handle the access control logic. However, this third party is lacking in transparency and could harm user privacy. Therefore, providing a distributed access control solution, while considering transparency and privacy-preserving awareness in IoT smart systems, is of paramount importance. The described issue can be addressed using the emergent Blockchain technology that provides a promising choice to build a new generation of decentralized and transparent access control solutions. This paper proposes a smart contract-based access control framework for IoT smart healthcare systems, which is based on smart contracts to provide a distributed and trustworthy access control, combined with the GTRBAC model to express fine-grained access control policies while considering temporal authorization constraints. To prove the feasibility and validity of the proposed framework, this paper also provides a detailed technical description and an initial implementation and execution. An experimental evaluation shows that security properties' analyses on smart contracts achieved the best possible evaluation with no vulnerabilities found, and the cost of access control operations increases linearly as the number of policy constraints increases. Besides, a comparative analysis reveals that the proposed approach can achieve good results with low gas costs and latency. [ABSTRACT FROM AUTHOR]

BORON is a lightweight substitution–permutation network cipher proposed in 2017. We reduce the number of guessed key bits by key-bridging technology and first utilize Fast Walsh Transform on BORON to minimize the time complexity. Finally, this paper gives the better key-recovery attack against block cipher BORON than previously proposed by 2 rounds: we realize a 11-round key-recovery attack on BORON-80 and 13-round key-recovery attack on BORON-128. The attacks proposed in this paper are the best attacks against BORON-80/128 to date. [ABSTRACT FROM AUTHOR]

A confidential reporting system (CRS) allows reporters to report concerns or problems in confidence without the fear of blame or reprisals. Nevertheless, privacy has been the primary concern of reporters. In this paper, we propose a privacy-preserving confidential reporting system with designated reporters (PPCRS-DR) to protect the privacy of reporters and the confidentiality of reports. Our PPCRS-DR provides the following interesting features: (1) for an event, an auditor can designate a reporter to report; (2) an auditor can neither see the report nor know the reporter's identity from an encrypted report if the reporter is not the designated one; (3) when an auditor is unavailable, he/she can temporarily designate a delegatee to collect and review reports on behalf of him/her. We formalize both the definition and security model of our PPCRS-DR, and propose a concrete construction. Furthermore, the security of the proposed PPCRS-DR is formally proven. The implementation shows that it is efficient. The novelty is to implement flexible decryption delegation of CRSs and protect reporters' privacy. [ABSTRACT FROM AUTHOR]

Mashups are web applications that expedite software development by reusing existing resources through integrating multiple application programming interfaces (APIs). Recommending the appropriate APIs plays a critical role in assisting developers in building such web applications easily and efficiently. The proliferation of publicly available APIs on the Internet has inspired the community to adopt various models to accomplish the recommendation task. Until present, considerable efforts have been made to recommend the optimal set of APIs, delivering fruitful results and achieving varying recommendation performance. This paper presents a timely review on the topic of API recommendations for mashup creation. Specifically, we investigate and compare not only traditional data mining approaches and recommendation techniques but also more recent approaches based on network representation learning and deep learning techniques. By analyzing the merits and pitfalls of existing approaches, we pinpoint a few promising directions to address the remaining challenges in the current research. This survey provides a timely comprehensive review of the API recommendation research and could be a useful reference for relevant researchers and practitioners. [ABSTRACT FROM AUTHOR]

Deep neural networks are vulnerable to backdoor attacks. Previous backdoor attacks have mainly focused on images. Unlike images composed of regular pixels, 3D point clouds are composed of irregular three-dimensional XYZ coordinates, which are widely used in areas such as autonomous driving and 3D measurement. As many deep neural networks have been developed for processing 3D point clouds, these networks also face the risk of backdoor attacks. Nevertheless, backdoor attacks on 3D point clouds have rarely been investigated. This paper proposes a stealthy backdoor attack on point clouds in the physical world, aiming to generate trainable non-rigid deformations as backdoor patterns. Instead of directly adding backdoor patterns onto the point clouds, we deform the 3D space of the point clouds to a new space, ensuring that all point clouds have the same backdoor deformation. We use point cloud alignment to overcome the inconsistency of backdoor deformation caused by shifting and scaling in the physical world. We also propose a physical transformation layer to combat the physical transformations. Additionally, we propose mask contrast learning to eliminate pseudo backdoor patterns to make the network's backdoor property stealthier. Extensive experiments indicate that the proposed method can achieve better attack success rates and stealthiness. [ABSTRACT FROM AUTHOR]

Violence detection is a critical task aimed at identifying violent behavior in video by extracting frames and applying classification models. However, the complexity of video data and the suddenness of violent events present significant hurdles in accurately pinpointing instances of violence, making the extraction of frames that indicate violence a challenging endeavor. Furthermore, designing and applying high-performance models for violence detection remains an open problem. Traditional models embed extracted spatial features from sampled frames directly into a temporal sequence, which ignores the spatio-temporal characteristics of video and limits the ability to express continuous changes between adjacent frames. To address the existing challenges, this paper proposes a novel framework called ACTION-VST. First, a keyframe extraction algorithm is developed to select frames that are most likely to represent violent scenes in videos. To transform visual sequences into spatio-temporal feature maps, a multi-path excitation module is proposed to activate spatio-temporal, channel and motion features. Next, an advanced Video Swin Transformer-based network is employed for both global and local spatio-temporal modeling, which enables comprehensive feature extraction and representation of violence. The proposed method was validated on two large-scale datasets, RLVS and RWF-2000, achieving accuracies of over 98 and 93%, respectively, surpassing the state of the art. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the first generic framework of policy-based remote user authentication from multiple biometrics. The proposed framework allows an authorized user to remotely authenticate herself to an authentication server using her multiple biometrics, which enhances both the security and usability of user authentications. The authentication server approves a user's authentication request if and only if the user's multiple biometrics satisfies an authentication policy. In particular, the authentication policy can be dynamically updated to satisfy different security and usability requirements in practice. We implement an instantiation of the proposed framework and report its performance under various authentication policies. [ABSTRACT FROM AUTHOR]

Recent studies show that neural networks are vulnerable to backdoor attacks, in which compromised networks behave normally for clean inputs but make mistakes when a pre-defined trigger appears. Although prior studies have designed various invisible triggers to avoid causing visual anomalies, they cannot evade some trigger detectors. In this paper, we consider the stealthiness of backdoor attacks from input space and feature representation space. We propose a novel backdoor attack named sparse backdoor attack, and investigate the minimum required trigger to induce the well-trained networks to make incorrect results. A U-net-based generator is employed to create triggers for each clean image. Considering the stealthiness of the trigger, we restrict the elements of the trigger between −1 and 1. In the aspect of the feature representation domain, we adopt an entanglement cost function to minimize the gap between feature representations of benign and malicious inputs. The inseparability of benign and malicious feature representations contributes to the stealthiness of our attack against various model diagnosis-based defences. We validate the effectiveness and generalization of our method by conducting extensive experiments on multiple datasets and networks. [ABSTRACT FROM AUTHOR]

Two vertices |$u$| and |$v$| in a graph |$G=(V,E)$| are in the same orbit if there exists an automorphism |$\phi $| of |$G$| such that |$\phi (u)=v$|⁠. The orbit number of a graph |$G$|⁠ , denoted by |$Orb(G)$|⁠ , is the smallest number of orbits, which form a partition of |$V(G)$|⁠ , in |$G$|⁠. All vertex-transitive graphs |$G$| are with |$Orb(G)=1$|⁠. Since the |$n$| -dimensional hypercube, denoted by |$Q_{n}$|⁠ , is vertex-transitive, it follows that |$Orb(Q_{n})=1$| for |$n\geq 1$|⁠. Pai, Chang, and Yang proved that the |$n$| -dimensional folded crossed cube, denoted by |$FCQ_{n}$|⁠ , is vertex-transitive if and only if |$n\in \{1,2,4\}$|⁠ , namely |$Orb(FCQ_{1})=Orb(FCQ_{2})=Orb(FCQ_{4})=1$|⁠. In this paper, we prove that |$Orb(FCQ_{n})=2^{\lceil \frac{n}{2}\rceil -2}$| if |$n\geq 6$| is even and |$Orb(FCQ_{n}) = 2^{\lceil \frac{n}{2}\rceil -1}$| if |$n\geq 3$| is odd. [ABSTRACT FROM AUTHOR]

The SM4 block cipher is a Chinese national standard and an ISO international standard. Since white-box cryptography has many real-life applications nowadays, a few white-box implementations of SM4 has been proposed, among which a type of constructions is dominated, which uses a linear or affine diagonal block encoding to protect the original three 32-bit branches entering a round function and uses its inverse as the input encoding to the S-box layer. In this paper, we analyse the security of this type of constructions against Lepoint et al.'s collision-based attack method. Our experiment under a small fraction of (encodings, round key) combinations shows that the rank of the concerned linear system is much less than the number of the involved unknowns, meaning these white-box SM4 implementations should resist Lepoint et al.'s method, but we leave it as an open problem whether there are such encodings that the rank of the corresponding linear system is slightly less than the number of the involved unknowns, in which scenario Lepoint et al.'s method may be used to recover a round key for the case with linear encodings and to remove most white-box operations until mainly some Boolean masks for the case with affine encodings. [ABSTRACT FROM AUTHOR]

As a structural topological index, the number of subtrees has great significance for the analysis and design of hybrid locally reliable networks. In this paper, with generating function and introducing a novel two-forest dual transformation technique, we solve the subtree enumerating problems of two representatives of the self-similar networks, such as the hierarchical lattice and |$(u,v)$| -flower networks. Moreover, by means of the circle weight transfer technique, two linear time algorithms of computing the subtree generation functions of these two families of networks are also proposed. The subtree density of two special cases for these self-similar networks is briefly discussed as an application. [ABSTRACT FROM AUTHOR]

Meta-learning is a pivotal and potentially influential machine learning approach to solve challenging problems in reinforcement learning. However, the costly hyper-parameter tuning for training stability of meta-learning is a known shortcoming and currently a hotspot of research. This paper addresses this shortcoming by introducing an online and easily trainable hyper-parameter optimization approach, called Meta Parameters Learning via Meta-Learning (MPML), to combine online hyper-parameter adjustment scheme into meta-learning algorithm, which reduces the need to tune hyper-parameters. Specifically, a basic learning rate for each training task is put forward. Besides, the proposed algorithm dynamically adapts multiple basic learning rate and a shared meta-learning rate through conducting gradient descent alongside the initial optimization steps. In addition, the sensitivity with respect to hyper-parameter choices in the proposed approach are also discussed compared with model-agnostic meta-learning method. The experimental results on reinforcement learning problems demonstrate MPML algorithm is easy to implement and delivers more highly competitive performance than existing meta-learning methods on a diverse set of challenging control tasks. [ABSTRACT FROM AUTHOR]

Chinese grammatical error correction (CGEC) is a significant challenge in Chinese natural language processing. Deep-learning-based models tend to have tens of millions or even hundreds of millions of parameters since they model the target task as a sequence-to-sequence problem. This may require a vast quantity of annotated corpora for training and parameter tuning. However, there are currently few open-source annotated corpora for the CGEC task; the existing researches mainly concentrate on using data augmentation technology to alleviate the data-hungry problem. In this paper, rather than expanding training data, we propose a competitive CGEC model from a new insight for reducing model parameters. The model contains three main components: a sequence learning module, a grammatical generalization module and a parameter sharing module. Experimental results on two Chinese benchmarks demonstrate that the proposed model could achieve competitive performance over several baselines. Even if the parameter number of our model is reduced by 1/3, it could reach a comparable |$F_{0.5}$| value of 30.75%. Furthermore, we utilize English datasets to evaluate the generalization and scalability of the proposed model. This could provide a new feasible research direction for CGEC research. [ABSTRACT FROM AUTHOR]

While tensor accelerated compilers have proven effective in deploying deep neural networks (DNN) on general-purpose hardware, optimizing for FPGA remains challenging due to the complex DNN architectures and the heterogeneous, semi-open compute units. This paper introduces the Automatic Kernel Generation for DNN on CPU-FPGA (AKGF) framework for efficient deployment of DNN on heterogeneous CPU-FPGA platforms. AKGF generates an intermediate representation (IR) of the DNN using TVM's Halide IR, annotates the operators of model layers in the IR to compute them on the corresponding hardware cores, and further optimizes the operator code for CPU and FPGA using ARM's function library and the polyhedral model to enhance model inference speed and power consumption. The experimental tests conducted on a CPU-FPGA board validate the effectiveness of AKGF, demonstrating significant acceleration ratios (up to 6.7x) compared to state-of-the-art accelerators while achieving a 2x power optimization. AKGF effectively leverages the computational capabilities of both CPU and FPGA for high-performance deployment of DNN on CPU-FPGA platforms. [ABSTRACT FROM AUTHOR]

Knowledge graph (KG) is introduced as side information into recommender systems, which can alleviate the sparsity and cold start problems in collaborative filtering. Existing studies mainly focus on modeling users' historical behavior data and KG-based propagation. However, they have the limitation of ignoring noise information during recommendation. We consider that noise exists in two parts (i.e. KG and user-item interaction data). In this paper, we propose Knowledge-aware Dual-Channel Graph Neural Networks (KDGNN) to improve the recommendation performance by reducing the noise in the recommendation process. Specifically, (1) for the noise in KG, we design a personalized gating mechanism, namely dual-channel balancing mechanism, to block the propagation of redundant information in KG. (2) For the noise in user-item interaction data, we integrate personalized and knowledge-aware signals to capture user preferences fully and use personalized knowledge-aware attention to denoise user-item interaction data. Compared with existing KG-based methods, we aim to propose a knowledge-aware recommendation method from a new perspective of denoising. We perform performance analysis on three real-world datasets, and experiment results demonstrate that KDGNN achieves strongly competitive performance compared with several compelling state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Random numbers are very important for the security of computer system. However, generating qualified random numbers is difficult because we cannot always successfully introduce dedicated random number hardware into computer system. Although most operating systems provide random number generation capabilities, the effective entropy supply is still dependent on the hardware platform including memory and clocks etc. However, obtaining hardware events such as clocks requires system privileges, which is not conducive for entropy estimation at the application layer. In contrast, data related to the sensor hardware can be extracted directly at the application layer. These sensor data contain some randomness and may be used as a noise source. In this way, applications can use these sensors to implement their own proprietary random number generators. Before taking these sensors as the noise source, it is necessary to fully evaluate their entropy supply capability. In this paper, 300 Android smartphones and 30 iOS smartphones are selected as samples and their sensor entropy supply capabilities are comprehensively evaluated. Based on the entropy evaluation results, we give some suggestions on how to generate random numbers using these sensor data. We first design a framework for evaluating the entropy supply capability for smartphone sensors, based on the min-entropy estimation method proposed in NIST SP 800-90B. According to this framework, we simulate stationary and mobile working states for each smartphone, and collect sufficient sensor data as the min-entropy estimation dataset. The min-entropy estimation results show that in the stationary working state, each ACCELEROMETER sensor data collection can obtain at least 1.5 bits of entropy in Android, while each GYROSCOPE sensor data collection can obtain at least 20 bits of entropy in iOS. In the mobile working state, each ACCELEROMETER sensor data collection can obtain at least 1.9 bits of entropy, while each GYROSCOPE sensor data acquisition in iOS system can obtain at least 27 bits of entropy. This means that we can still get a stable entropy output from the sensor even when the smartphone is in stationary working state. Statistical analysis of the data using cross correlation methods suggests it is hard for an attacker to guess or predict the random numbers generated by a smartphone through another smartphone put in the similar external environment. [ABSTRACT FROM AUTHOR]

Secure exchange of data among the various stake holders of healthcare systems is of prime importance. As the size of the healthcare networks grew, several variants of Public Key Infrastructures (PKIs) were proposed as a means to achieve reliable authentication, confidentiality, non-repudiation, etc. The most prevalent approach to PKI has been the use of Certificate Authorities (CAs). But, events like the breach of the CA DigiNotar, and the ensuing fake certificates for Google, among other noteworthy high-profile domains, has cast doubts on the reliability of a CA, and therefore on PKIs modelled as CAs too. In this paper, we propose a new approach to a healthcare PKI modelled on a blockchain, incorporating Elliptic Curve Cryptographic methods for secure key generations. [ABSTRACT FROM AUTHOR]

The key to deep learning vulnerability detection framework is pre-processing source code and learning vulnerability features. Traditional source code representation techniques take a complete normalization to user-defined symbols but ignore the semantic information associated with vulnerabilities. The current mainstream vulnerability feature learning model is Recurrent Neural Network (RNN), whose time-series structure determines its insufficient remote information acquisition capability. This paper proposes a new vulnerability detection framework to solve the above problems. We propose a new data normalization method in the source code pre-processing phase. The user-defined symbols are clustered using the unsupervised clustering algorithm K-means. The normalized classification is performed according to the clustering results, which preserves the primary semantic information in the source code and ensures the smoothness of the sample data. In the feature extraction stage, we input the source code after performing text representation into Bidirectional Encoder Representations for Transformers (BERT) for feature automation learning, which enhances semantic information extraction and remote information acquisition. Experimental results show that the vulnerability detection precision of this method is 18.3% higher than that of the current mainstream vulnerability detection framework in the real-world data collected by ourselves. Further, our method improves the precision of the state-of-the-art method by 4.2%. [ABSTRACT FROM AUTHOR]

Named Data Networking (NDN) is an emerging network architecture. An important characteristic of NDN is its in-network cache, which enables Data packets to be available from multiple locations on the Internet. Hence the enforcement of access control mechanisms becomes even more critical in the NDN. This paper proposes a novel access control scheme referred to as Role-Based Encryption for NDN (NDN-RBE), which uses a broadcast encryption mechanism to achieve secure data access control. Our scheme uses the role inheritance property of the traditional Role-Based Access Control (RBAC) model to achieve efficient data access control over hierarchical content. This makes our scheme particularly suitable for large-scale real-world content-centric services like Netflix. Our scheme also supports additional design features such as anonymous signature-based authentication, batch signature verification and two types of privilege revocations. In addition, our formal security analysis demonstrates that our scheme is provably secure against Chosen Plaintext Attacks. Our performance and functionality comparison show that our scheme outperforms other notable existing works in terms of security, functionality, computation, communication and storage overhead. Furthermore, our experimental results show an improvement in content delivery time of the order of 15 percent compared with the other closely related works. [ABSTRACT FROM AUTHOR]

Fault-tolerant performance of a network is the prerequisite and guarantee for the normal operation of a network, which is often characterized by connectivity. Let |$H$| denote a connected subgraph of |$G$| and |$H^{*}$| denote the union of the set of all connected subgraphs of |$H$| and the set of the trivial graph. Super |$H$| -connectivity (resp. super |$H^{*}$| -connectivity) satisfies the conditions of both super connectivity and |$H$| -structure connectivity (resp. |$H$| -substructure connectivity). These two kinds of new connectivity provide a new metric to measure the fault-tolerance of the network, that is, the super structure fault-tolerance. The generalized hypercube |$G(m_{r}, m_{r-1},... m_{1})$| is a universal topology of interconnection networks that contains other commonly used topologies and it has been applied in many data center networks because of its excellent qualities. In this paper, we research the super structure fault-tolerance of |$G(m_{r}, m_{r-1},... m_{1})$| by studying super |$H$| -connectivity |$\kappa ^{\prime}(G|H)$| and super |$H^{*}$| -connectivity |$\kappa ^{\prime}(G|H^{*})$| for |$H\in \{K_{1,M},\ C_{3},\ C_{4},\ K_{4}\}$|⁠. [ABSTRACT FROM AUTHOR]

With Internet access shifting from desktop-driven to mobile-driven, application-level mobile traffic identification has become a research hotspot. Although considerable progress has been made in this research field, two obstacles are hindering its further development. Firstly, there is a lack of sharable labeled mobile traffic datasets. Although it is easy to capture mobile traffic, labeling traffic at the application level is non-trivial. Besides, researchers usually hold a conservative attitude toward publishing their datasets for privacy concerns. Secondly, most of the datasets used by existing studies are inadequate to evaluate the proposed methods, since they usually have the problems of inaccurate labels, small scale and simple collection configurations. To tackle these two obstacles, a mobile traffic collection is carried out in this paper. The collected traffic has the advantages of large-scale data size, accurate application-level labels and diverse collection configurations. Then, the collected traffic is anonymized carefully to make it public. Several mobile traffic identification methods are compared based on our anonymized dataset, which proves the applicability of our dataset. [ABSTRACT FROM AUTHOR]

The high energy density of solar energy gives wireless sensor networks advantages in outdoor monitoring applications. However, long-term stable monitoring is challenging due to frequent weather changes, shading by buildings and trees, etc. The existing research usually uses two technologies to solve the above problems: (1) the energy prediction algorithm, and (2) the energy-aware routing strategy. However, in an actual deployment, frequent weather changes can significantly reduce the accuracy of the existing prediction algorithms. When using the algorithms as the support for energy-aware routing, the network lifetime is less than ideal. The existing routing strategies are in need of further improvement. Because of its lack of environmental adaptability, nodes consume energy quickly and have a high mortality rate. Therefore, aiming at the long-term stability of solar wireless sensor networks, this paper proposes a prediction algorithm based on classification and recurrent neural networks, and integrates the shadow judgement method from our previous research to correct the predicted values. Furthermore, we propose a routing optimization model that can flexibly adjust the target according to the solar intensity. The experimental results show that the prediction and routing scheduling algorithm can significantly improve the energy prediction accuracy (30–50%) and prolong the network lifetime (10–42%) in outdoor small sensor scenarios. [ABSTRACT FROM AUTHOR]

In truncated differential cryptanalysis of symmetric primitives, a generalized framework is to search a distinguisher concerning part of output differences, like truncated differential distribution (TDD) on certain bits (e.g. a nibble) first, and then append several rounds before and after it to recover the secret key. The logarithmic likelihood ratio statistic with respect to the TDD is usually used to distinguish guessed key bits. In this paper, we study how to improve the effect of truncated differential cryptanalysis by considering key schedules of the attacked ciphers. It turns out that for a cipher with a simple key schedule, certain guessed subkey bits may reveal information of the master key, which will help build a stronger TDD distinguisher and reduce the key recovery complexity or attack more rounds. As a result, we explore heuristic techniques to search key-recovery-friendly TDDs and construct automatic search models based on MILP. The refined methods are applied to two recent designs of symmetric primitives, WARP and Orthros, together with peculiarities of their structures as well. For WARP, after making two observations on relations between certain differences with key bits, we propose an algorithm that can find TDDs with low complexities and having potentialities to cover more rounds. Consequently, we launch key recovery attacks on 24 to 27 rounds of WARP. When it comes to Orthros, we present a two-step search algorithm to balance the number of guessed key bits and TDDs, obtaining a key recovery attack on a 7-round variant of it in the weak-key setting. Finally, we perform several verification experiments on round-reduced versions of WARP and Orthros, and the experimental results are consistent with the theoretical distributions and the analysis of generalized key recovery attack framework. [ABSTRACT FROM AUTHOR]

GIFT is a lightweight cipher proposed by Banik et al. at CHES'17, motivated by the design strategy of PRESENT. GIFT-64[2021] is a variant of GIFT proposed by Sun et al. at EUROCRYPT'22 to achieve better resistance against differential attack while maintaining a similar security level against linear attack. At EUROCRYPT'22, Dong et al. proposed a new rectangle framework considering the key guessing strategies for linear key-schedule ciphers, and established a uniform automatic search model for the whole rectangle attack. In this paper, we extend it to be applicable to bit-oriented ciphers, and construct an automatic search model involved in the distinguisher and key-recovery phase for GIFT. Moreover, we utilize the key relations of the linear key-schedule to the model, and find some new distinguishers both for GIFT-64 and GIFT-64[2021]. To evaluate the probability more accurately, we propose a method to calculate the probability of the 2-round middle part which connects the boomerang distinguisher for GIFT , and apply it with the SAT method to evaluate the probability of the whole distinguishers. As a result, we search out a new 20-round related-key boomerang distinguisher for GIFT-64 , and achieve a 26-round attack with better time complexity than the best previous attack. For GIFT-64[2021] , we find a 20-round boomerang distinguisher and give the first 26-round rectangle attack under related-key scenario. [ABSTRACT FROM AUTHOR]

It had always been believed that there was an inherent barrier to Differential Fault Attack (DFA) on the nonce-based authenticated encryption algorithm. At CHES 2016, Saha et al. proposed an Internal Differential Fault Attack on a parallelizable counter-mode algorithm. They induce the attack to classical DFA at the expense of one more fault injection in every encryption process. In this paper, we propose the DFA on HYENA, which is a nonce-based authenticated encryption mode for GIFT-128. Our work is the first pure classical DFA on a nonce-based authenticated encryption algorithm with only one fault injected in every decryption process. Firstly, we give the DFA on GIFT-128 with a fault injected into the 39th-round input. Based on this work, we inject a fault in the underlying GIFT-128 of a HYENA decryption process and make this decryption process still generate the correct tag and output plaintext. This makes the necessary conditions of DFA satisfied. Experiments show that at most 56 key bits of HYENA can be recovered with only a few faulty ciphertexts. In addition, our fault injection is easier to achieve than most other work about fault attack, because the injection location is relatively random and the fault type can be arbitrary. It should be noted that the left 72 key bits cannot be recovered in this way. [ABSTRACT FROM AUTHOR]

In comparison with capturing channel state information (CSI) measurements via a laptop or desktop, using a smartphone to collect CSI measurements incurs the restriction of working with a single access point and significant signal distortions, resulting in limited information for smartphone localization. Therefore, this paper intends to leverage as much available localization information as possible by (⁠|$1$|⁠) shifting the WiFi frequency from |$2.4$| to |$5$| GHz; (⁠|$2$|⁠) calibrating the noisy CSI measurements and (⁠|$3$|⁠) fusing both amplitudes and phases of the CSI measurements, so as to enhance localization accuracy. Specifically, we first filter out distorted CSI measurements based on their distribution characteristics, then apply the advanced uniform manifold approximation and projection method to refine the mapping relations from a high-dimensional fingerprint space to a low-dimensional location space, and design a location fusion algorithm based on the continuous feature scaling model, which is able to distinguish two locations with similar fingerprints. Extensive experimental results show that the localization accuracy of the proposed approach outperforms the state-of-the-art counterparts by at least |$15.5$| and |$18.7\%$| using two off-the-shelf smartphones. [ABSTRACT FROM AUTHOR]

Vision Transformers for pre-trained models explore semantic context and spatial relationships for images, which heavily depend on how you select image patches. In this paper, we propose a novel Spatial-aware Multi-directional Patches Multi-cycle Autoencoder (SMPMA) for a pre-trained model that brings the following benefits: (1) Spatial-aware Multi-directional (SM) patches are created with multi-directional spatial locations, transforming a whole image autoencoder problem into a short-span image patches autoencoder problem; (2) SM patches admit a self-cycle autoencoder alignment learning for the first stage and a cross-cycle interaction learning for the second stage, which makes patches align and interact for optimization; (3) SM patches enable to explore local object features and correlation distribution of adjacent pixels by taking arbitrary sampled patches as inputs. Experimental results on four downstream tasks show that our model can achieve state-of-the-art performance over the tasks of image generation, image classification and semantic segmentation. [ABSTRACT FROM AUTHOR]

The low transaction capacity, high transaction cost and long-term privacy concerns of the current Ethereum platform are notorious. Developers are seeking alternative blockchain platforms to migrate their blockchain-based applications to reduce their applications' use-cost and improve their applications' user experience. The Hyperledger Fabric (HLF) platform with resiliency, flexibility, scalability and confidentiality is preferred for developers to migrate their Ethereum blockchain-based applications. However, it is laborious for developers to migrate blockchain-based applications from the Ethereum platform to the HLF platform. In this paper, we first propose a complete and secure migration solution to ease the migration process. The main idea of our solution is to design a toolbox to help developers automatically eliminate the adverse effects that the differences between Ethereum and HLF may bring to the migrated application. Developers with the toolbox can migrate the application with little time and minimal modification. It is theoretically proved that the migrated application with the toolbox is secure. Besides, a prototype of the toolbox is implemented. The extensive experiments demonstrate that the time for the migration process is acceptable, and the toolbox has little impact on the migrated application's performance. [ABSTRACT FROM AUTHOR]

Recently, 5G has become an important mechanism to serve a growing amount of users in the network, which provides enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) services. Meanwhile, Carrier Aggregation (CA) is a promising 5G technology which lets users aggregate fragmented carrier components (CCs) to enhance transmission efficiency. This paper addresses 5G scheduling problem in the CA-enabled eMBB–URLLC coexistence network using genetic algorithm (GA). The key idea is first treating the serving time slot of each user equipment (UE) as a gene and combining all genes to form a chromosome, then generating a set of chromosomes as the initial population and letting the population evolve to iteratively select the fittest one as the resource scheduling decision. Up to now, this is the first work using GA for solving the scheduling problem in the CA-enabled 5G network. Simulation results show that our proposed method can serve the most UEs (⁠|$4\%$|⁠) with the highest throughput (⁠|$7.1\%$|⁠) and lower delay. [ABSTRACT FROM AUTHOR]

In most threshold Elliptic Curve Digital Signature Algorithm (ECDSA) signatures using additively homomorphic encryption, the zero-knowledge (ZK) proofs related to the ciphertext or the message space are the bottleneck in terms of bandwidth as well as computation time. In this paper, we propose a compact ZK proof for relations related to the Castagnos–Laguillaumie (CL) encryption, which is 33% shorter and 29% faster than the existing work in PKC 2021. We also give new ZK proofs for relations related to homomorphic operations over the CL ciphertext. These new ZK proofs are useful to construct a bandwidth-efficient universal composable-secure threshold ECDSA without compromising the proactive security and the non-interactivity. In particular, we lowered the communication and computation cost of the key refresh algorithm in the Paillier-based counterpart from |$O(n^3)$| to |$O(n^2)$|⁠. Considering a 5-signer setting, the bandwidth is better than the Paillier-based counterpart for up to 99, 95 and 35% for key generation, key refreshment and pre-signing, respectively. [ABSTRACT FROM AUTHOR]

Mobile devices (MDs) are becoming more prevalent and their battery life is optimised by offloading tasks to cloud servers. However, communication costs must be considered when offloading tasks. To make task offloading worthwhile, it is important to measure the energy consumed during communication activities. Thus, a heterogeneous framework is developed to enhance the energy efficiency of smartphones by analysing parameters such as task and non-task offloading, local cloudlets, radio access networks and remote cloud servers. This paper proposes a task offloading framework that uses a novel algorithm, the Hybrid Red Fox Flow Direction-based Ensemble SVM Forest Classifier, to enhance the system parameters and schedule tasks in offloading cloud computing conditions. The multi-objective function aims to improve user satisfaction by maximising resource utilisation and minimising function. The framework was tested in the Cloudsim simulation tool and compared with different techniques, with the results demonstrating its superiority in terms of energy efficiency and system performance. The proposed framework can optimise the energy efficiency of MDs and improve battery life. [ABSTRACT FROM AUTHOR]

Dragonfly networks have significant advantages in data exchange due to the small network diameter, low cost and modularization. A graph |$G$| is |$vertex$| - |$pancyclic$| if for any vertex |$u\in V(G)$|⁠ , there exist cycles through |$u$| of every length |$\ell $| with |$3\leq \ell \leq |V(G)|$|⁠. A graph |$G$| is |$Hamiltonian$| - |$connected$| if there exists a Hamiltonian path between any two distinct vertices |$u,v\in V(G)$|⁠. In this paper, we mainly research the pancyclic and Hamiltonian properties of the dragonfly network |$D(n,h)$|⁠ , and find that it is Hamiltonian with |$n\geq 1,\,\,h\geq 2$|⁠ , pancyclic, vertex-pancyclic and Hamiltonian-connected with |$n\geq 4,\,\,h\geq 2$|⁠. [ABSTRACT FROM AUTHOR]

As a key issue of network virtualisation, virtual network embedding (VNE) aims to embed multiple virtual network requests (VNRs) from different applications onto the substrate network effectively. In real networks, about 90% of traffic is generated by different quality of service (QoS) sensitive applications. However, most existing VNE algorithms do not account for the difference. Although several VNE algorithms considered the delay metric of applications, they usually provide strict delay guarantees for all VNRs, leading to a low VNR acceptance ratio. In this paper, we focus on the VNE problem involving multiple QoS metrics and propose a multiple QoS metrics-aware VNE algorithm based on reinforcement learning (RLQ-VNE). We first classify VNRs according to their different requirements for multiple QoS metrics including delay, jitter and packet loss rate, and then introduce reinforcement learning to implement differentiated VNE. Specifically, RLQ-VNE provides strict QoS guarantees for the VNRs with high-level QoS requirements and provides lower QoS guarantees for the VNRs with low-level QoS requirements, thus balancing the QoS guarantee and request acceptance ratio. Simulation results from multiple experimental scenarios show that RLQ-VNE improves the request acceptance ratio and network resource utilisation by sacrificing less QoS. [ABSTRACT FROM AUTHOR]

Byzantine ordered consensus , introduced by Zhang et al. (OSDI 2020), is a new consensus primitive that additionally guarantees a correctness specification of transaction order, allowing nodes to assign fairly an ordering indicator to the committed transaction. Zhang et al. also presented a concrete Byzantine ordered consensus protocol called Pompē in the partially synchronous network model. However, Pompē cannot prevent an adversary from manipulating message delivery time. In this paper, we present Chronos, the first Byzantine ordered consensus protocol in the asynchronous network model, where an adversary can arbitrarily manipulate message delivery time. To construct Chronos, we propose a variant of asynchronous common subset called signal asynchronous common subset protocol, which guarantees the liveness of Chronos. We implement both Chronos and its baseline HoneyBadgerBFT using Go language and deploy them on 100 Amazon t3.medium instances distributed throughout 10 regions across the world. The experimental results show that Chronos is more efficient than HoneyBadgerBFT for small network, achieving peak throughput of 59 368 tx/s when the batch size is 100 000 and the number of nodes is 4, while the peak of HoneyBadgerBFT is 57 077 tx/s. [ABSTRACT FROM AUTHOR]

Commitment scheme is a fundamental cryptographic primitive that serve as building blocks for many other two-party protocols. In this paper, we propose a novel quantum bit commitment scheme, which is secure and does not require quantum memory. Our scheme processes the quantum information using coherent states and unambiguous state discrimination (USD) measurements, which can be experimentally realized by linear optics and photon detectors. We ensure the unconditionally hiding and binding property by preventing both Alice and Bob from obtaining complete information about the commitment in committing stage, and eliminate the requirement for quantum memory by performing USD measurements and phase shifts immediately after receiving the coherent states. [ABSTRACT FROM AUTHOR]

White-box implementation can ensure the security of cryptographic algorithm in white-box attack environment without changing the inputs and outputs of the original algorithm. Most existing white-box implementations construct a series of lookup tables to protect the key. However, with the development of white-box attack techniques, many white-box implementations have been proved to be insecure. In this paper, a new white-box implementation of SM4 is proposed, which is based on an equivalent partial SPN structure of the SM4 algorithm. Our implementation includes three types of table lookup operations and XOR operations. The round keys are obfuscated with the self-equivalences of the S-box and random affine encodings. Security analysis shows that our implementation can resist BGE-type attack, the attack based on affine equivalence algorithm, the structure attack, the collision attack and differential computational analysis. Furthermore, our scheme requires 8.125 MB of memory. [ABSTRACT FROM AUTHOR]

The MinRank problem is an NP-complete problem that is prevalent in multivariate cryptography and its goal is to find a non-zero linear combination of given a series of matrices over a ring such that the obtained matrix has a small rank. At Asiacrypt 2001, two Zero-Knowledge Proofs of Knowledge (ZKPoK) for the MinRank problem are proposed, and we call them MRZK and MRZK |$^{\dagger }$|⁠ , respectively. The latter is an improved version of the proof size of the former. However, the efficiency of MRZK |$^{\dagger }$| has been open and not analyzed. While the MRZK protocol is secure, it must be repeated many times due to the soundness error |$2/3$|⁠ , which leads to the large proof size. For 128-bit security, the MRZK protocol is executed at least 219 iterations and the proof size is about 32 KB. In this paper, we first show that the efficiency of MRZK |$^{\dagger }$| is impractical due to unreasonable parameter size. However, when the parameter size is tuned and the efficiency is improved, an imposter can be efficiently constructed. Then, to alleviate the large proof size of MRZK, inspired by the technique designing ZKPoK (Eurocrypt 2020), we propose a sigma protocol with helper to prove the solution to the MinRank problem. Finally, we transform the sigma protocol with helper into a standard ZKPoK (MRZK |$^{\sharp }$|⁠) by removing the helper. The MRZK |$^{\sharp }$| protocol can achieve any small soundness error and enjoy the proof size of about 15 KB (53% improvement over MRZK). [ABSTRACT FROM AUTHOR]

This paper investigates the problem of resilient vector consensus for a group of dynamic agents against mobile malicious attacks. As real networks often operate under random environment and noises, we approach this problem by considering general random dynamic networks with weighted directed topologies. We propose three types of mobile attack models, which differ in the timing of moving of attackers and the capability of detecting such moving. By employing distributed discrete-time algorithms, the Lyapunov theory and martingale convergence theorem, resilient vector consensus is shown to be reached for all three models when the underlying network satisfies certain stochastic robustness conditions. [ABSTRACT FROM AUTHOR]