Reviewerly/exHarmony huggingface dataset:
Privacy Enhancement Via Dummy Points in the Shuffle Model

The shuffle model is recently proposed to address the issue of severe utility loss in Local Differential Privacy (LDP) due to distributed data randomization. In the shuffle model, a shuffler is utilized to break the link between the user identity and the message uploaded to the data analyst. Since less noise needs to be introduced to achieve the same privacy guarantee, following this paradigm, the utility of privacy-preserving data collection is improved. We propose DUMP ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DUM</u> my- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> oint-based), a framework for privacy-preserving histogram estimation in the shuffle model. The core of DUMP is a new concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dummy blanket</i> , which enables enhancing privacy by just introducing dummy points on the user side and further improving the utility of the shuffle model. We instantiate DUMP by proposing two protocols: pureDUMP and mixDUMP, and conduct a comprehensive experimental evaluation to compare them with existing protocols. The experimental results show that, under the same privacy guarantee, (1) the proposed protocols have significant improvements in communication efficiency over all existing multi-message protocols, by at least 3 orders of magnitude; (2) they achieve competitive utility, while the only known protocol (Ghazi <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , PMLR 2020) having better utility than ours employs hard-to-exactly-sample distributions which are vulnerable to floating-point attacks (CCS 2012).
A Lightweight Face Recognition Algorithm for Internet of Vehicles

In recent years, face detection and recognition technology has been widely used in many important scenarios, especially for automatic driving scenarios and future Internet of vehicles. Practical challenges such as computing resource limitation and real-time processing of edge nodes need to be fully considered. In order to cope with the problem of severe computing resources limitation on the edge of the Internet of Vehicles, this paper optimized the lightweight network model Lightened CNN for face depth feature extraction. By the batch normalization mechanism, the vector distribution of facial features of different identities is dispersed. The relative feature center distance is increased. It gives the extracted features a better classification aggregation feature. According to the design flow of face recognition algorithm, the method of face alignment and similarity measurement is studied. It is designed to complete the face recognition scheme adapted to the Internet of Vehicles scene. After experiment and analysis, the face depth feature extraction network designed in this paper occupies less embedded system resources. At the same time, the face recognition algorithm designed by combining lightweight face feature extraction network, and face alignment and similarity measurement has higher accuracy.
Reed-Solomon Coding Algorithms Based on Reed-Muller Transform for Any Number of Parities

Based on the Reed-Muller (RM) transform, this paper proposes a Reed-Solomon (RS) encoding/erasure decoding algorithm for any number of parities. Specifically, we first generalize the previous RM-based syndrome calculation, which allows only up to seven parities, to support any number of parities. Then we propose a general encoding/erasure decoding algorithm. The proposed encoding algorithm eliminates the operations in solving linear equations, and this improves the computational efficiency of existing RM-based RS algorithms. In terms of erasure decoding, this paper employs the generalized RM-based syndrome calculation and lower–upper (LU) decomposition to accelerate the computational efficiency. Analysis shows that the proposed encoding/erasure decoding algorithm approaches the complexity of <inline-formula><tex-math notation="LaTeX">$\lfloor \lg T \rfloor + 1$</tex-math></inline-formula> XORs per data bit with <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> increasing, where <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> denote the number of parities and codeword length respectively. To highlight the advantage of the proposed RM-based algorithms, the implementations with Single Instruction Multiple Data (SIMD) technology are provided. Simulation results show that the proposed algorithms are competitive, as compared with other cutting-edge implementations.
Transferable Adversarial Attack on 3D Object Tracking in Point Cloud

3D point cloud tracking has recently witnessed considerable progress with deep learning. Such progress, however, mainly focuses on improving tracking accuracy. The risk, especially considering that deep neural network is vulnerable to adversarial perturbations, of a tracker being attacked is often neglected and rarely explored. In order to attract attentions to this potential risk and facilitate the study of robustness in point cloud tracking, we introduce a novel transferable attack network (TAN) to deceive 3D point cloud tracking. Specifically, TAN consists of a 3D adversarial generator, which is trained with a carefully designed multi-fold drift (MFD) loss. The MFD loss considers three common grounds, including classification, intermediate feature and angle drifts, across different 3D point cloud tracking frameworks for perturbation generation, leading to high transferability of TAN for attack. In our extensive experiments, we demonstrate the proposed TAN is able to not only drastically degrade the victim 3D point cloud tracker, i.e., P2B [21], but also effectively deceive other unseen state-of-the-art approaches such as BAT [33] and M $$^{2}$$ Track [34], posing a new threat to 3D point cloud tracking. Code will be available at https://github.com/Xiaoqiong-Liu/TAN .
Fast Syndrome-Based Chase Decoding of Binary BCH Codes Through Wu List Decoding

We present a new fast Chase decoding algorithm for binary BCH codes. The new algorithm reduces the complexity in comparison to a recent fast Chase decoding algorithm for Reed–Solomon (RS) codes by the authors (IEEE Trans. IT, 2022), by requiring only a single Kötter iteration per edge of the decoding tree. In comparison to the fast Chase algorithms presented by Kamiya (IEEE Trans. IT, 2001) and Wu (IEEE Trans. IT, 2012) for binary BCH codes, the polynomials updated throughout the algorithm of the current paper typically have a much lower degree. To achieve the complexity reduction, we build on a new isomorphism between two solution modules in the binary case, and on a degenerate case of the soft-decision (SD) version of the Wu list decoding algorithm. Roughly speaking, we prove that when the maximum list size is 1 in Wu list decoding of binary BCH codes, assigning a multiplicity of 1 to a coordinate has the same effect as flipping this coordinate in a Chase-decoding trial. The solution-module isomorphism also provides a systematic way to benefit from the binary alphabet for reducing the complexity in bounded-distance hard-decision (HD) decoding. Along the way, we briefly develop the Gröbner-bases formulation of the Wu list decoding algorithm for binary BCH codes, which is missing in the literature.
Low-Complexity Code Clone Detection using Graph-based Neural Networks

Code clone detection is of great significance for intellectual property protection and software maintenance. Deep learning has been applied in some research and achieved better performance than traditional methods. To adapt to more application scenarios and improve the detection efficiency, this paper proposes a low-complex code clone detection with the graph- based neural network. As the input of the neural network, code features are represented by abstract syntax trees (ASTs), in which the redundant edges are removed. The operation of pruning avoids interference in the message passing of the network and reduces the size of the graph. Then, the graph pairs for the code clone detection are sent into the message passing neural networks (MPNN). In addition, the gated recurrent unit (GRU) is used to learn the information between graph pairs to avoid the operation of Graph mapping. After multiple iterations, the attention mechanism is used to read out the graph vector, and the cosine similarity is calculated on the graph vector to obtain the code similarity. Through the experiments on two datasets, the results show that the proposed clone detection scheme removes about 20 % of the redundant edges and reduces 25 % of model weights, 16% of multiply-accumulate operations (MACs). In the end, the proposed method effectively reduces the training time of graph neural network while presenting a similar performance to the baseline network.
On the Effect of Solution Representation and Neighborhood Definition in AutoML Fitness Landscapes

The interest in AutoML search spaces has given rise to a plethora of studies conceived to better understand the characteristics of these spaces. Exploratory landscape analysis is among the most commonly investigated techniques. However, in contrast with other classical optimization problems, in AutoML defining the landscape may be as tough as characterizing it. This is because the concept of solution neighborhood is not clear, as the spaces have a high number of conditional hyperparameters and a somehow hierarchical structure. This paper looks at the impact of different solution representations and distance metrics on the definition of these spaces, and how they affect exploratory landscape analysis metrics. We conclude that these metrics are not able to deal with structured, complex spaces such as the AutoML ones, and problem-related metrics might be the way to leverage the landscape complexity.
Blockchain-Based Fairness-Enhanced Federated Learning Scheme Against Data Poisoning Attack

The federated learning technology provides a new method for data integration, which realizes sharing of a global model and prevent the leakage of user’s original data information. In order to resist data poisoning attack from some participants, ensure reliability and accuracy of the global model, and ensure fairness of the aggregation process in federated learning, we propose a blockchain-based fairness enhanced federated learning scheme. The accuracy of global model and fairness of the aggregation process is guaranteed by an adaptive aggregation algorithm which can defense data poisoning attack. The reliability of federated learning process is ensured by recording the entire process of the model training on the blockchain and using digital signatures. The privacy of each participant of federated learning is protected by public key encryption combined with the use of random numbers. Theoretical analysis and experiments show that the scheme can protect privacy of each participant, mitigate data poisoning attack and ensure the reliability and fairness of the entire federated learning process.
PPFM: An Adaptive and Hierarchical Peer-to-Peer Federated Meta-Learning Framework

With the advancement in Machine Learning (ML) techniques, a wide range of applications that leverage ML have emerged across research, industry, and society to improve application performance. However, existing ML schemes used within such applications struggle to attain high model accuracy due to the heterogeneous and distributed nature of their generated data, resulting in reduced model performance. In this paper we address this challenge by proposing PPFM: an adaptive and hierarchical Peer-to-Peer Federated Meta-learning framework. Instead of leveraging a conventional static ML scheme, PPFM uses multiple learning loops to dynamically self-adapt its own architecture to improve its training effectiveness for different generated data characteristics. Such an approach also allows for PPFM to remove reliance on a fixed centralized server in a distributed environment by utilizing peer-to-peer Federated Learning (FL) framework. Our results demonstrate PPFM provides significant improvement to model accuracy across multiple datasets when compared to contemporary ML approaches.
Truthful Auction Mechanism for Data Trading with Share-Averse Data Consumers

In the paper we focus on a promising research problem of data trading, under the scenario that data items can be reproduced easily and inexpensively. Apart from a Bayesian optimal mechanism based data trading approach, we also propose a prior-free data trading approach to organize the data trading process between selfish data owners and share-averse data consumers, with the goal of maximizing revenue of data owners and meanwhile determining the optimal number of data copies. Rigorous theoretical analysis and extensive experiment results are offered to verify the effectiveness of proposed methods in terms of sum of valuations, revenue, individual rationality and incentive compatibility.
Blockchain Applications in Smart City: A Survey

Smart cities bring new ideas to solve the social, economic, and environmental problems existing in traditional cities. However, the services of smart cities suffer from centralized data storage and untrustworthiness. As a decentralized distributed database, blockchain provides distributed and trusted infrastructure technology for the construction of smart cities. In this survey, we conduct a brief survey of the literature on blockchain applications in smart cities. We review the application of blockchain in smart cities from the four main application scenarios of energy, transportation, medical care, and manufacturing. Then, we discuss the realization of blockchain-based smart cities from the perspectives of privacy and storage. We believe this survey provides new ideas for the development of smart cities.
Efficient and Flexible Multiauthority Attribute-Based Authentication for IoT Devices

The correctness and reliability of data sources are the keys to the practicality of data collected by Internet of Things (IoT) devices. Attribute-based signature (ABS) is a cryptographic primitive for users to sign with their own attributes, which can be applied to the authentication process in IoT scenarios. The attribute authority is responsible for issuing the attribute key to the user in ABS. Multiple authorities can complete attribute management tasks to avoid the threat of a single authority. However, attribute authorities need to execute multiple interactions to collaborate to generate attribute keys for users, which brings a large transmission burden. In addition, a lot of resource-constrained terminals in the IoT mostly play the role of signer or verifier in authentication protocols. The signature generation and verification algorithms often have heavy pairing and exponentiation operations. Currently, no ABS scheme takes into account the efficiency of all participating entities simultaneously. In this article, we present an aggregated anonymous key issue (AAKI) protocol to reduce the transmission burden between multiple authorities. Meanwhile, the noninteractive zero-knowledge proof aggregate exponentiation (NI-ZKPoKAE) protocol is designed to aggregate the transmitted secret values in AAKI. To reduce the computational burden of signers and verifiers, Blakley secret sharing, where the Hadamard matrix is used more efficiently to handle the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n, n)$ </tex-math></inline-formula> -threshold, is used to construct an efficient and fine-grained multiauthority ABS (EFMA-ABS) scheme. This brings high efficiency to all three types of parties involved in IoT authentication. Our above-mentioned protocols have been proven to be feasible and effective.
BiDETECT: BiLSTM with BERT for hate speech detection in tweets

The utilization of online platforms for spreading hate speech has become a major concern. The conventional techniques used to identify hate speech, such as relying on keywords and manual moderation, frequently fall short and can lead to either missed detections or incorrect identifications. In response, researchers have developed various deeplearning strategies for locating hate speech in text. This paper covers a wide range of Deep Learning approaches, encompassing Convolutional Neural Networks and especially transformer-based models. It also discusses the key factors that influence the performance of these methods, such as the choice of datasets, the use of pre-processing strategies, and the design of the model architecture. In conjunction with summarizing existing research, it also identifies a selection of key hurdles and limitations of Deep Learning for discovering hate speech and has proposed a novel method to overcome them. In Bidirectional Long Short-Term Memory and BERT for Hate Speech Detection (BiDETECT), which involves adding a Bidirectional Long Short-Term Memory (BiLSTM) layer to Bidirectional Encoder Representations from Transformers (BERT) for classification, the hurdles include the difficulties in defining hate speech, the limitations of current datasets, and the challenges of generalizing models to new domains. It also discusses the ethical implications of employing Deep Learning to pinpoint hate speech and the need for responsible and transparent research in this area.
Data-Centric Perspective on Explainability Versus Performance Trade-Off

The performance versus interpretability trade-off has been well-established in the literature for many years in the context of machine learning models. This paper demonstrates its twin, namely the data-centric performance versus interpretability trade-off. In a case study of bearing fault diagnosis, we found that substituting the original acceleration signal with a demodulated version offers a higher level of interpretability, but it comes at the cost of significantly lower classification performance. We demonstrate these results on two different datasets and across four different machine learning algorithms. Our results suggest that "there is no free lunch," i.e., the contradictory relationship between interpretability and performance should be considered earlier in the analysis process than it is typically done in the literature today; in other words, already in the preprocessing and feature extraction step.
Extended Dependency Modeling Technique for Cyber Risk Identification in ICS

Complex systems such as Industrial Control Systems (ICS) are designed as a collection of functionally dependent and highly connected units with multiple stakeholders.Identifying the risk of such complex systems requires an overall view of the entire system.Dependency modelling (DM) is a highly participative methodology that identifies the goals and objectives of a system and the required dependants to satisfy these goals.Researchers have proved DM to be suitable for identifying and quantifying impact and uncertainty in complex environments.However, there exist limitations in the current expressions of DM that hinder its complete adaptation for risk identification in a complex environment such as ICS.This research investigates how the capability of DM could be extended to address the identified limitations and proposes additional variables to address phenomena that are unique to ICS environments.The proposed extension is built into a system-driven ICS dependency modeller, and we present an illustrative example using a scenario of a generic ICS environment.We reflect that the proposed technique supports an improvement in the initial user data input in the identification of areas of risk at the enterprise, business process, and technology levels.
Artificial Neural Network Based Type-2 Fuzzy Optimization for Medical Diagnosis

In this work, we generalized the notion of fuzzy logic and neural network in order to develop type-2 neuro fuzzy system. With the help of proposed system, we will be able to deal medical problem to enhance the performance for reimbursing the higher uncertainties. For the validity of proposed system, we are giving numerical trials to justify our approach. Further, the parameters involve in the output of the proposed system is optimized by using teaching learning-based optimization (TLBO).
Do Not Rely on Clock Randomization: A Side-Channel Attack on a Protected Hardware Implementation of AES

Clock randomization is one of the oldest countermeasures against side-channel attacks. Various implementations have been presented in the past, along with positive security evaluations. However, in this paper we show that it is possible to break countermeasures based on a randomized clock by sampling side-channel measurements at a frequency much higher than the encryption clock, synchronizing the traces with pre-processing, and targeting the beginning of the encryption. We demonstrate a deep learning-based side-channel attack on a protected FPGA implementation of AES which can recover a subkey from less than 500 power traces. In contrast to previous attacks on FPGA implementations of AES which targeted the last round, the presented attack uses the first round as the attack point. Any randomized clock countermeasure is significantly weakened by an attack on the first round because the effect of randomness accumulated over multiple encryption rounds is lost.
Discovering Diverse Top-K Characteristic Lists

In this work, we define the new problem of finding diverse top-k characteristic lists to provide different statistically robust explanations of the same dataset. This type of problem is often encountered in complex domains, such as medicine, in which a single model cannot consistently explain the already established ground truth, needing a diversity of models. We propose a solution for this new problem based on Subgroup Discovery (SD). Moreover, the diversity is described in terms of coverage and descriptions. The characteristic lists are obtained using an extension of SD, in which a subgroup identifies a set of relations between attributes (description) with respect to an attribute of interest (target). In particular, the generation of these characteristic lists is driven by the Minimum Description Length (MDL) principle, which is based on the idea that the best explanation of the data is the one that achieves the greatest compression. Finally, we also propose an algorithm called GMSL which is simple and easy to interpret and obtains a collection of diverse top-k characteristic lists.
A Novel Blockchain Framework for Enhancing Online Transactions

In the modern era, blockchain technology has essentially functioned in diverse facilities ranging from online applications like payment transactions, data transactions, healthcare information sharing, supply chain tracking, etc. The research is developed for online applications to exploit the blockchain concept in recording large data transactions. During the transaction, privacy protection is an important concern for securing the data in the cloud database. For this reason, numerous cryptographic algorithms were developed to end this issue. Therefore, in this research, a novel Homomorphic Serpent Blockchain (HSB) algorithm is developed to secure online transactions while avoiding security threats. Consequently, the performance of the proposed algorithm is compared with conventional techniques and attained the finest outcomes with high accuracy and less time duration.