Reviewerly/exHarmony huggingface dataset:
Verifiable Credentials with Privacy-Preserving Tamper-Evident Revocation Mechanism

Verifiable Credential (VC) is a new standard proposed by the W3C association to facilitate the expression and verification of third-party-verified credentials on the Internet, such as passports or diplomas. However, the current VC data model lacks an explicit revocation design that guarantees the secure operations of the system, which limits its application. In this paper, we specify the requirements for a tamper-evident and privacy-preserving revocation mechanism, based on which we compare existing solutions and propose our revocation mechanism that satisfies all the requirements. Our design combines a cryptographic accumulator and a role-based blockchain. With zero-knowledge proof, the verifier can operate off-chain computation of the revocation status while ensuring the correctness of revocation information published on the blockchain. Our analysis shows that the proposed revocation mechanism can prevent fraud using forged and revoked credentials and relieve privacy concerns caused by the correlation of digital data. Our proof-of-concept implementation demonstrates that our revocation mechanism adds only 42.86 ms overhead in the presentation and 31.36 ms overhead in the verification of verifiable credentials. We also provide scalability analysis, which illustrates that the throughput of our blockchain can meet real-world needs.
A Reputation System for Distributed Intelligent Blockchain Decision-Making

Discerning ground-truth amidst misinformation is a nontrivial task in decentralized networks. While trivial for traditional blockchain networks using Proof of Work (PoW) consensus, it requires immense computational resources at the cost of network scalability and therefore has compatibility challenges with limited devices like Internet of Things (IoT) networks. Moving towards a lightweight alternative - a novel scheme is proposed for provable identification of reputable nodes for making intelligent decisions using distributed machine-learning optimization, called Weight-Based Reputation (WBR). A distributed learning task identifies nodes that are willing to cooperate as a means for securing ground-truth, and is provably resistant to data poisoning in theory and experimentation. A novel method combining WBR with a classifier creates lightweight and robust blockchain decision-making. The results of this two-step approach is a novel method for predicting various attacks that result transaction log, such as fraud or the double-spend. A Bayesian game against adversary manipulation formulation and experimental results show superior performance and robustness to the state-of-the-art. Resource consumption compared against blockchain protocols is also provided. Future work will explore WBR in distributed consensus as a complete alternative to blockchain PoW.
A Blockchain Node Organizing and Auto-Scaling Architecture for the Internet of Things

Blockchain has recently received great attention in non-monetary fields such as Internet of Things (IoT) due to its noticeable peculiarities including decentralization, security and immutability. Most conventional blockchain designs have limited throughput and large transaction delays. The majority of the proposed IoT-based blockchain models lack the neces-sary scalability to handle the massive amount of transactions produced by IoT devices, as well as the mechanisms that take control decentralization and managing node overhead capacity into account. In this paper, we propose: i) a multi-tiered self-scalable dynamic (SSD) architecture that limits the number of nodes participating in consensus and the number of hops needed through clustering all nodes, ii) a logical positioning mechanism for locating nodes in O (1), iii) a high efficiency dynamic cyclic application-agnostic scaling algorithm, SSD algorithm, that helps SSD structure to shrink and/or expand automatically, conserving scalability and security by maintaining a load balance between control decentralization and node overhead capacity and iv) a colocality mechanism that can be leveraged. The architecture along with the proposed algorithm and mechanisms help reduce consensus time: in O(1) plus twice O(maximum propagation delay towards topmost nodes), whose number is a recursive logarithmic reduction of the number of nodes directly managing IoT devices, worst case. As a proof of concept, we have created simulations in Python3 for SSD model and SSD algorithm and the results rank SSD model to exceed traditional as well as two-tiered models.
Socio-Technical Principles of Decentralized Protocol Design

Work in security within the computer sciences normally focuses on achieving certain technical properties via design. Yet recently there has been a rise in interest in the broader social effects of technology leading to design principles being understood as normative and social. Using work around decentralization, in particular blockchain technology, as our guiding example, we will explicate how socio-technical principles can enable designers of decentralized technical systems to understand the social remit of their design space. We will present a schema for distinguishing between design principles, properties and effects in the area of decentralization to show how socio-technical principles can be systematized in practice.
Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning

The continuous thriving of the Blockchain society motivates research in novel designs of schemes supporting cryptocurrencies. Previously multiple Proof-of-Deep-Learning(PoDL) consensuses have been proposed to replace hashing with useful work such as deep learning model training tasks. The energy will be more efficiently used while maintaining the ledger. However deep learning models are problem-specific and can be extremely complex. Current PoDL consensuses still require much work to realize in the real world. In this paper, we proposed a novel consensus named Proof-of-Federated-Learning-Subchain(PoFLSC) to fill the gap. We applied a subchain to record the training, challenging, and auditing activities and emphasized the importance of valuable datasets in partner selection. We simulated 20 miners in the subchain to demonstrate the effectiveness of PoFLSC. When we reduce the pool size concerning the reservation priority order, the drop rate difference in the performance in different scenarios further exhibits that the miner with a higher Shapley Value (SV) will gain a better opportunity to be selected when the size of the subchain pool is limited. In the conducted experiments, the PoFLSC consensus supported the subchain manager to be aware of reservation priority and the core partition of contributors to establish and maintain a competitive subchain.
Blockchain-Based Consent Management for Privacy Persevering and Transparency in Intelligent Surveillance Systems

Video cameras are becoming ubiquitous. They'd be interconnected within a larger pervasive infrastructure supported by artificial intelligence which helps ensure smart services relaying in real-time video analysis and image processing. However, such services and technologies involve a set of privacy concerns. If privacy concerns can be addressed, enabling real-time analytics on video streams in public spaces can be valuable in supporting many applications for security, public safety, and urban space management. This study aims to highlight privacy concerns in image and video processing in the context of intelligent IoT systems by proposing a blockchain-based approach for data transparency and privacy management. As a result, our proposed approach will provide IoT customers the ability to manage their consent using a distributed ledger securely.
Towards Citizen-Centric Services using Blockchain-Powered Digitalization of Public Sector Processes

Public sector institutions operate in a rapidly changing landscape that is transforming the way services are delivered. To ensure positive outcomes for citizens, governments need the support of efficient public institutions with the capacity to keep track with emerging trends and changing expectations. The adoption of new technologies offers innovative opportunities for the public sector and can improve interactions between citizens and institutions and enabling citizen-centric services. The emergence of blockchain technology has inspired a new type of information exchange infrastructure on a peer-to-peer model that makes possible a set of use cases in sectors where improved efficiency, reliability, security and costs around transparency are intended. The public sector is mainly characterized by administrative processes that involve several institutions. If blockchain can promote various types of integration in an organization, the implications around its applicability and its interoperability in the complex integration of an inter-organizational public process remain to be demonstrated. In this paper, we investigate a real-life use case in public administration related to the vehicle registration process involving multiple institutions to develop an architecture that makes possible a blockchain-based digitalization of the inter-organizational process. We describe a case study and use it to extract the system requirements, analyze an approach that identifies coordination and data interoperability in public sector inter-organizational processes, and propose an architecture focused on transparency and privacy for effective deployment. We then implement the designed architecture around the scenarios of the vehicle registration.
Faster Secure Matrix Multiplication with the BGV Algorithm

In cloud computing, preserving the privacy of sensitive data poses security challenges. Homomorphic encryption offers a viable solution by enabling computations on encrypted data. Among essential operations in diverse applications, secure matrix multiplication plays a key role, particularly in statistical analysis and machine learning algorithms. While multiplying two matrices by the secure matrix multiplication, the algorithm constructs packed polynomials, encrypts them, and performs homomorphic multiplication. However, performing homomorphic multiplication on large matrices encounters difficulties due to the significant increase in the degree of packed polynomials. To enhance the efficiency of secure matrix multiplication, this paper proposes an approach that combines the Strassen algorithm and the secure matrix multiplication with the Brakerski, Gentry, and Vaikuntanathan (BGV) homomorphic encryption scheme after dividing matrices into smaller blocks. This integration offers several advantages, including reducing the number of required homomorphic encryptions from eight to seven when employing one-level recursion and better performance with smaller parameters. Our experimental results demonstrate that the Strassen method yields superior performance outcomes for large dimensions. For instance, when considering a 128 × 128 matrix and utilizing a submatrix size of two, we observe that the Strassen secure block matrix algorithm is 44% faster than the standard secure block matrix multiplication.
Differential and Linear Cryptanalysis of IVLBC via MILP Modeling

Involutive Lightweight Block Cipher (IVLBC) is a recently proposed SPN-type block cipher. The intended platform for this cipher is the Internet of Things (IoT), and its performance is promising for both software and hardware. Preliminary differential and linear cryptanalysis are conducted via Mixed-Integer Linear Programming (MILP), an automated tool in the design paper. In this paper, we employ the MILP approach to identify the best linear and differential characteristics of IVLBC. For differential cryptanalysis, we discover a characteristic with a probability of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−46</sup> over 7 rounds of IVLBC, while in linear cryptanalysis, we identify a characteristic with a bias of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−24</sup> , again for 7 rounds of the cipher. These results provide the best single-key differential and linear distinguishers of this cipher in the literature.
Audio Forgery Detection Method with Mel Spectrogram

With the development of technology, making forged digital audio gets easier. Copy-move is a popular way to create audio forgeries. Copy move audio forgery is carried out by copying a word and pasting it on another word in the same audio. It gets harder to detect the forged area in the same audio because of the similar external factors. Also post-processing operators such as noise addition, compression etc are applied to audios by attackers after creating forged audio to make it even harder to detect forged area. This is another situation that makes detection difficult in practice. In this work, we used mel spectrogram representation of the audios. The proposed algorithm to detect forgery consists of four stages: keypoint detection, word-level feature description and matching, phrase-level feature description, and post-processing. The experimental results show that the proposed method is highly robust to attacks and gives higher accuracy than other studies in the literature.
Comparison of Top 10 Well-Known Blockchain Consensus Algorithms

In a blockchain network, it is necessary to run a consensus algorithm to make the network secure and work properly. While many blockchain applications are developed in various fields from industries to financial purposes, it is vital to understand the insight of the fundamentals of consensus algorithms and its variants to measure these applications’ applicability. There are several consensus algorithms and each of these algorithms have different performance and security features. There is no consensus algorithm to ensure the best performance for every application. Therefore, different blockchain platforms and their consensus algorithms from the top 10 layer 1 chains listed on Coinmarketcap have chosen to benchmark these consensus algorithms. We have compared these consensus algorithms to point out their own characteristics pros and cons by the aspects of maximum transaction per second (max TPS), time of transaction finality (Tx Finality), tolerance for attacks, centralization, number of validators, transaction cost (Tx Cost) and hardware costs of its nodes. Considering the consensus of top 10 well-known blockchains in coinmarketcap, their performance relative to each other will be evaluated.
Methods for Masking CRYSTALS-Kyber Against Side-Channel Attacks

In the context of post-quantum secure algorithms like CRYSTALS-Kyber, the importance of protecting sensitive polynomial coefficients from side-channel attacks is increasingly recognized. Our research introduces two alternative masking methods to enhance the security of the compression function in Kyber through masking. Prior to this, the topic had been addressed by only one other research study. The "Double and Check" method integrates arithmetic sharing and symmetry adjustments, introducing a layer of obfuscation by determining coefficient values based on modular overflows. In contrast, the Look-Up-Table (LUT) integration method employs arithmetic-to-Boolean conversions, augmented by a pre-computed table for efficient value verifications. Furthermore, by leveraging the alternative prime 7681, we propose a novel masked compression function. This prime, 7681, is also notable as the smallest prime suitable for fast NTT multiplication. While both algorithms prioritize data protection and streamlined processing, they also underscore the inherent challenges of balancing computational speed with the potential vulnerabilities to side-channel attacks.
Developing Explainable Intrusion Detection Systems for Internet of Things

The Internet of Things (IoT) is a pivotal concept that supports the revolutionizing 5G/6G, smart cities, and industrial internet technologies. Although IoT brings countless opportunities for innovation, it is accompanied with several cybersecurity issues and vulnerabilities, as well. In order to cope with these issues, machine learning (ML) based intrusion detection systems (IDSs) are developed to detect unauthorized actions with high accuracy rates. However, the results of these systems cannot be interpreted by the users and cybersecurity experts, since employing ML techniques in a black-box manner is deficient in explaining which particular decisions are made during the detection processes. Explainable Artificial Intelligence (XAI) is an emerging paradigm providing interpretability on the decisions of ML-based IDSs to overcome these limitations. In this paper, we have developed explainable IDSs (X-IDSs) for IoT networks by combining ML and XAI concepts. We have used Logistic Regression (LR), Random Forest (RF) and Decision Tree (DT) algorithms for attack detection purposes. In order to provide explainability for the decision-making process of IDSs, we have applied LIME and SHAP, two popular XAI methods, on the results of the developed IDSs. The results show that our IDSs outperform the works in the literature in terms of accuracy rate, and they provide both local and global explanations to interpret ML-based decisions.
Stealthy Attacks on Multi-Agent Reinforcement Learning in Mobile Cyber-Physical Systems

Due to their mobility, real-time requirements, energy limitations, and safety considerations, the complexities involved in Mobile Cyber-Physical Systems (MCPSs) surpass those of traditional computing systems. To address these challenges, the use of multi-agent reinforcement learning (MARL) algorithms is gaining significance in the field of MCPS. MARL enables precise, instantaneous, and coordinated decision-making to maximize cumulative rewards through systematic trial and error, even in unfamiliar environments. While MARL algorithms can effectively learn scalable and efficient control policies for MCPSs, their resilience against security and safety attacks has not been thoroughly explored, severely limiting their real-world applications. This paper investigates the robustness of MARL-based MCPS against stealthy adversarial attacks which involve targeting and manipulating a specific mobile node in order to generate deceptive observations that adversely affect the behavior of other MCPS nodes. We adopt the FGSM (Fast Gradient Sign Method) adversarial example technique from deep learning to incorporate a detection evasion mechanism as a new stealth feature. The objective is to entice the compromised node to adopt an adversarial policy that deviates the activations of policy networks in its cooperative nodes from the expected distribution, while evading detection. We empirically demonstrate the susceptibility of MARL algorithms commonly employed in MCPSs, namely MADDPG, to our proposed attack strategies. The evaluation is conducted in three MCPSs, considering both white and black-box settings. By targeting a single node, our attacks have a significantly detrimental impact on the overall performance of the MCPS, resulting in a minimum reduction of 33% and a maximum reduction of 89.6% in the system's overall reward, with an evasion rate ranging from 16% to 36%.
Towards More Secure Virtual Reality Authentication for the Metaverse: A Decentralized Method Proposal

Virtual reality (VR) headsets enable us to enter the virtual worlds and the emerging Metaverse. VR authentication becomes important for the user’s information privacy and security in the virtual world. Current authentication methods have known vulnerabilities such as predictable passwords and theft of biometric information. We propose a decentralized method based on self-sovereign identity (SSI). The proposed method differs with an additional factor that involves the usage of users’ memories. Scenes are used to provide immutable and unpredictable secret information. These images are stored on IPFS, and associated links are recorded on the blockchain. A proof of concept prototype is implemented, and the ease of use is taken into account. The results are promising. The paper concludes with a discussion on challenges, improving this method, and future work.
Feature Engineering for Injection Attack Detection: An Exploration from SQLI to XSS

This paper explores the effects of data preprocessing and feature engineering on injection attacks using a classification and anomaly detection approach. To this end, the proposed data preprocessing and feature engineering method is compared against state-of-the-art SQL injection detection on two different datasets using KNN, DT and SVM as classifiers, whereas OCSVM as an anomaly detector. Evaluation results of the classifiers and the anomaly detector not only show the effectiveness and high performance of the proposed data preprocessing and feature engineering method on SQL injection attacks but also demonstrate the generalizability on other injection and non-injection attacks.
Adversarial Security and Differential Privacy in mmWave Beam Prediction in 6G networks

In the forthcoming era of 6G, the mmWave communication is envisioned to be used in dense user scenarios with high bandwidth requirements, that necessitate efficient and accurate beam prediction. Machine learning (ML) based approaches are ushering as a critical solution for achieving such efficient beam prediction for 6G mmWave communications. However, most contemporary ML classifiers are quite susceptible to adversarial inputs. Attackers can easily perturb the methodology through noise addition in the model itself. To mitigate this, the current work presents a defensive mechanism for attenuating the adversarial attacks against projected ML-based models for mmWave beam anticipation by incorporating adversarial training. Furthermore, as training 6G mmWave beam prediction model necessitates the use of large and comprehensive datasets that could include sensitive information regarding the user's location, differential privacy (DP) has been introduced as a technique to preserve the confidentiality of the information by purposefully adding a low sensitivity controlled noise in the datasets. It ensures that even if the information about a user location could be retrieved, the attacker would have no means to determine whether the information is significant or meaningless. With ray-tracing simulations for various outdoor and indoor scenarios, we illustrate the advantage of our proposed novel framework in terms of beam prediction accuracy and effective achievable rate while ensuring the security and privacy in communications.
Better Technology, but Less Realism: The Perplexing Development and Application of VTuber Technology

Virtual YouTubers (VTubers) are a type of digital human created by digital technology that has become a cultural phenomenon. As modeling, motion capture and rendering technologies have matured and hardware and software costs have fallen, it has become possible for individual VTubers to create high-quality simulated 3D avatars. However, the results of this study, which analyzed the images of the 400 most popular VTubers on both YouTube and Bilibili, found that the majority of VTubers still choose to use 2D animated avatars. We believe there are three reasons for this perplexing phenomenon: visual subjectivity, cost objectivity and psychological subjectivity.