Delegated Proof of Stake (DPoS) is a widely utilized consensus protocol in blockchain-based Internet of Things (IoT) systems. We propose a heuristic algorithm-based accounting rights allocation method, also referred to as the witness election method, which aims to address the challenges in DPoS. The challenges associated with selected witness nodes that do not reflecting majority stakeholder preferences and susceptibility to manipulation of the vote. This method employs the Kendallâs rank correlation as the fitness function to optimize the arrangement of the top-k producers, thereby maximizing stakeholder preferences. We propose a novel heuristic algorithm, termed SP-DEWOA, which combines the differential evolution algorithm and whale optimization with piecewise chaotic mapping to maximize permutation similarity, i.e., stakeholder preferences. To further improve the efficiency of SP-DEWOA, we parallelize SP-DEWOA based on the Spark-based parallelization design. Experimental results demonstrate that the witness nodes selected through SP-DEWOA are consistent with the preferences of the majority of stakeholders. Furthermore, SP-DEWOA has been proven to have high scalability and resilience against vote manipulation.
Rui Hong Gao, Zhiguo Wan, Huaqun Wang, Shaoteng Luo
Decentralized finance based on blockchain has experienced rapid development. To safeguard the privacy of participants, decentralized anonymous payment (DAP) systems such as ZCash and Zether have emerged. These systems employ cryptographic techniques to conceal the trader addresses and payment amounts. However, this anonymity presents challenges in terms of regulation. To address this issue, we propose the Walsh-DAP (WDAP) scheme, an efficient and generic regulation scheme for decentralized anonymous payments that strikes a balance between regulation and privacy preservation. Our scheme introduces two regulation policies: first, users who have exceeded their spending limits within a certain period will be identified during the regulation process; second, the supervisor possesses the capability to trace any anonymous transaction. To implement regulation effectively, we have designed an innovative commitment scheme, Walsh commitment, which leverages the orthogonal properties of Walsh codes to achieve the features of aggregatability and extractability. The supervisor in WDAP only needs to deal with the aggregation result of the Walsh commitments instead of the huge amount of raw transactions information, which greatly increases the efficiency. In a DAP system with 256 users, 10 transactions per second and 30 days as a regulation period, we reduced the communication cost for regulation from 14 GB to 94.20 KB, and the computing cost from$\text{1.6}\times \text{10}^{\text{5}}$s to 2.17s. Both improvement is of over five orders of magnitude. We formally discussed the security of the whole system, and verified its feasibility and practicability in the ZCash system.
Zeyu Zhou, Ding Liu, Tatiana R. Shmeleva, Dmitry A. Zaitsev
Bitcoin is under the threat of fork since it operates with a distributed ledger. Predicting the fork probability in advance is beneficial for taking early action to avoid malicious attacks. In this study, we compose a colored Petri net model of Bitcoin. Our model consists of a given number of nodes, and each node has five subpages representing the node structure: proof of work, broadcast blocks, verify blocks, and the process of adding blocks to blockchain, respectively. Simulation results of fork probability can be easily obtained and analyzed by observing the data in the measuring components of subpages. The results show that our model correctly simulates the fork probability: on recent Bitcoin data, compared with the results of the wide-known SimBlock simulator, a difference of some 4.3% has been obtained. Thus, taking into account vivid graphical representation, our model has certain advantages for the developing techniques of attack avoidance.
Advanced Steganography and Watermarking Techniques
Karanjot Singh Saggu, Paula Branco, Guy-Vincent Jourdan
The Bitcoin generator scam is one example of existing deceptive schemes enticing users with promises of free or effortless Bitcoin generation. These scams predominantly exploit individuals unfamiliar with cryptocurrency seeking low-effort avenues to obtain Bitcoin without financial investment. In this paper, we propose and analyze methods to improve the performance of Graph Neural Networks (GNNs) in detecting fraudulent cases within Bitcoin transactional data. We explore multiple GNN variants, alongside various graph sampling methodologies. To overcome the shortcomings of these sampling methods, we propose a new sampling method BFRONâa hybrid approach mixing Breadth-First Search and Frontier Sampling. Additionally, we introduce an enhanced optimization pipeline and a new metric to improve fraudulent node detection. Evaluation metrics, including Instance Information Gain and Group Distance Ratio, are employed to analyze the challenges of over-smoothing in Graph Neural Networks and the efficacy of diverse graph sampling techniques. Our results show that overall BFRON is the best solution and RGGCN is the best-performing GNN. Moreover, we show that our enhanced pipeline and the usage of graph normalization have important advantages.
Wenhan Hou, Bo Cui, Yongxin Chen, Ru Li · 5 authors
As a representative of the public blockchain, Ethereum has been applied in various industries. However, the vast number of transactions on the platform has also brought a number of illegal activities, such as phishing scams, which have caused significant damage to the Ethereum ecosystem. Due to anonymity of the blockchain, it is difficult for detectors to extract features that can be directly applied to phishing scams detection. Existing studies mainly model Ethereum transaction records as a network and mine key information from them to identify phishing addresses. However, these methods usually employ traditional feature engineering or network embedding, ignoring the fine-grained features in the transaction network. In addition, since the original network is too large to make learning difficult, existing work usually uses random walk (RW) to sample a part of nodes for training, thus ignoring the multiplicity of the network. To address these issues, in this article, we propose a three-stream feature fusion (TSFF) approach to enhance the feature representation of nodes. Specifically, we construct node states to guide RW sampling, and manually extracted 8-D features from the resulting dataset as basic features. Temporal features are jointly learned through long short-term memory network and contrastive learning. We combine residual blocks and graph convolutional network to extract fine-grained structural features from transactional networks. Finally, we fuse these three types of features and input them into a downstream classifier. Experiments show that our TSFF (85.3% Precision) outperforms the state-of-the-art methods, and the effectiveness of each feature is demonstrated.
Inderpreet Singh, Amandeep Kaur, Parul Agarwal, Sheikh Mohammad Idrees
Abstract Most existing e-government services are centralized and rely heavily on human control. This centralized approach makes the system more susceptible to external attacks and compromises data integrity by rogue insiders. Additionally, relying on individuals to monitor and control workflows introduces errors and corruption risks. In order to guarantee security and transparency, this study proposes an automated and decentralized online voting system that makes use of blockchain technology. Compared to conventional voting techniques, it is more efficient and cost-effective, because it eliminates the need of intermediaries. The primary goal of this research is to use blockchain technology to develop a transparent and safe online voting system. In this paper, a decentralized voting system will be developed utilizing ethereum blockchain and smart contracts to ensure the voting processâs integrity. The system can be evaluated with simulated voting data to reflect real-world scenarios, focusing on security, scalability, and user-friendliness. The study also explores potential future enhancements, such as incorporating biometric authentication to further improve accessibility and security. The insights provided will be valuable to policymakers, researchers, and practitioners involved in the development, implementation, and regulation of blockchain-based voting systems.
Open access
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
You-Hyun Kim, Ongee Jeong, Kevin Choi, Inkyu Moon · 5 authors
Zero-knowledge proof systems based on Feige-FiatâShamir (FFS) protocol are an interactive protocol between two anonymous authentication parties. However, they require heavy computations because of many iterations for reducing the probability that an attacker can trick a remote server. The algorithmâs time complexity rapidly increases with the total number of the challenge values, which should be unpredictable. Hence, the FFS protocol is not suitable for practical zero-knowledge proof systems. In this study, we propose new zero-knowledge proof systems based on phase mask generation that are complex sinusoidal waveform versions of the FFS algorithm for efficient anonymous authentication in the diverse interactive systems. The proposed anonymous authentication schemes need a single iteration only, allowing for efficient uses of a random challenge mask with large bit-depth. The proposed schemes allow the verifier to verify that the prover knows the secret mask, such as binary pattern, visual image, or hologram, which are the proverâs secrets, without revealing any information about it to anyone else, including the verifier. Various numerical simulations demonstrate the proposed schemesâ feasibility and robustness.
Matthew Sharp, Laurent Njilla, ChinâTser Huang, Tieming Geng
Advancements in blockchain technology and network technology are bringing in a new era in electronic voting systems. These systems are characterized by enhanced security, efficiency, and accessibility. In this paper, we compose a comparative analysis of blockchain-based electronic voting (e-voting) systems using blockchain technology, cryptographic techniques, counting methods, and security requirements. The core of the analysis involves a detailed examination of blockchain-based electronic voting systems, focusing on the variations in architecture, cryptographic techniques, vote counting methods, and security. We also introduce a novel blockchain-based e-voting system, which integrates advanced methodologies, including the Borda count and Condorcet method, into e-voting systems for improved accuracy and representation in vote tallying. The systemâs design features a flexible and amendable blockchain structure, ensuring robustness and security. Practical implementation on a Raspberry Pi 3 Model B+ demonstrates the systemâs feasibility and adaptability in diverse environments. Our study of the evolution of e-voting systems and the incorporation of blockchain technology contributes to the development of secure, transparent, and efficient solutions for modern democratic governance.
This article proposes a novel method for managing usage counters within an anonymous credential system, addressing the limitation of traditional anonymous credentials in tracking repeated use. The method takes advantage of blockchain technology through Smart Contracts deployed on the Ethereum network to enforce a predetermined maximum number of uses for a given credential. Users retain control over increments by providing zero-knowledge proofs (ZKPs) demonstrating private key possession and agreement on the increment value. This approach prevents replay attacks and ensures transparency and security. A prototype implementation on a private Ethereum blockchain demonstrates the feasibility and efficiency of the proposed method, paving the way for its potential deployment in real-world applications requiring both anonymity and usage tracking.
Nicolas Huber, Ralf KĂŒsters, Julian Liedtke, Daniel Rausch
Abstract Electronic voting (e-voting) systems have become more prevalent in recent years, but security concerns have also increased, especially regarding the privacy and verifiability of votes. As an essential ingredient for constructing secure e-voting systems, designers often employ zero-knowledge proofs (ZKPs), allowing voters to prove their votes are valid without revealing them. Invalid votes can then be discarded to protect verifiability without compromising the privacy of valid votes. General purpose zero-knowledge proofs (GPZKPs) such as ZK-SNARKs can be used to prove arbitrary statements, including ballot validity. While a specialized ZKP that is constructed only for a specific election type/voting method, ballot format, and encryption/commitment scheme can be more efficient than a GPZKP, the flexibility offered by GPZKPs would allow for quickly constructing e-voting systems for new voting methods and new ballot formats. So far, however, the viability of GPZKPs for showing ballot validity for various ballot formats, in particular, whether and in how far they are practical for voters to compute, has only recently been investigated for ballots that are computed as Pedersen vector commitments in an ACM CCS 2022 paper by Huber et al. Here, we continue this line of research by performing a feasibility study of GPZKPs for the more common case of ballots encrypted via Exponential ElGamal encryption. Specifically, building on the work by Huber et al., we describe how the Groth16 ZK-SNARK can be instantiated to show ballot validity for arbitrary election types and ballot formats encrypted via Exponential ElGamal. As our main contribution, we implement, benchmark, and compare several such instances for a wide range of voting methods and ballot formats. Our benchmarks not only establish a basis for protocol designers to make an educated choice for or against such a GPZKP, but also show that GPZKPs are actually viable for showing ballot validity in voting systems using Exponential ElGamal.
With the exponential growth of smart gadgets and the technologies that connect them, the Internet of Things (IoT) has emerged as the most promising new technology of the last decade. This is true from both a business and academic standpoint. Deployment in smart-home and smart-city initiatives throughout the globe has increased IoT's popularity. Regrettably, the processing power, storage capacity, and bandwidth of IoT network devices are severely constrained. So, compared to other endpoint devices like PCs, tablets, and smartphones, they are more susceptible to assaults. The development of a safe and reliable model for commonly used operations is booming, including data collection, processing, storage, and communication. It isn't easy to build such a model since there are so many obstacles to overcome. New cybersecurity risks emerge on a regular basis, while old ones remain dormant and ready to be exploited. Sadly, these cyber dangers also serve to safeguard the IoT. Due to recent developments, research into cyberphysical systems, the internet of things (IoT), and blockchain is more important than ever. In Internet of Things (IoT) systems, where security breaches compromise the centralised system's ability to function, blockchain technology has shown remarkable promise. Distributed ledgers powered by blockchain technology may enhance the security of the Internet of Things (IoT) and its user access management systems, making them more reliable. Following the safe vehicle authorization, the suggested model presents the idea of a branching blockchain that considers the most recently utilized block. This allows low-powered devices to also benefit from the blockchain's security. The needs of the IoV at the physical layer are beyond the capabilities of present-day mining technology. The suggested architecture has many important aspects, such as a decentralized ledger system, load balancing, scalability, and a lightweight blockchain for the physical layer and above. The comparison investigation, which takes computation and transmission costs into account, shows that our proposed framework performs better than the ones that are presently in use. If anybody is interested in a lightweight blockchain variation that can be applied to the physical layer of the IoV, this model may serve as the foundation for new research areas.
In this paper, we propose a smart contract-based multi-candidate self-tallying voting scheme in order to guarantee the privacy of ballots in the case of multiple candidates. This scheme uses the ElGamal cryptosystem to ensure the security of the ballots, and combines it with a Distributed Encryption algorithm to make the voting scheme have self-tallying features, and guarantees the correctness of the intermediate data through zero-knowledge proofs. The experimental results show that the scheme improves the voting efficiency without compromising the security.
In recent years, a more advanced form of phishing has arisen on Ethereum, surpassing early-stage, simple transaction phishing.This new form, which we refer to as payload-based transaction phishing (PTXPHISH), manipulates smart contract interactions through the execution of malicious payloads to deceive users.PTXPHISH has rapidly emerged as a significant threat, leading to incidents that caused losses exceeding $70 million in 2023 reports.Despite its substantial impact, no previous studies have systematically explored PTXPHISH.In this paper, we present the first comprehensive study of the PTXPHISH on Ethereum.Firstly, we conduct a long-term data collection and put considerable effort into establishing the first ground-truth PTXPHISH dataset, consisting of 5,000 phishing transactions.Based on the dataset, we dissect PTXPHISH, categorizing phishing tactics into four primary categories and eleven sub-categories.Secondly, we propose a rule-based multidimensional detection approach to identify PTXPHISH, achieving an F1-score of over 99% and processing each block in an average of 390 ms.Finally, we conduct a large-scale detection spanning 300 days and discover a total of 130,637 phishing transactions on Ethereum, resulting in losses exceeding $341.9 million.Our in-depth analysis of these phishing transactions yielded valuable and insightful findings.Scammers consume approximately 13.4 ETH daily, which accounts for 12.5% of the total Ethereum gas, to propagate address poisoning scams.Additionally, our analysis reveals patterns in the cash-out process employed by phishing scammers, and we find that the top five phishing organizations are responsible for 40.7% of all losses.Furthermore, our work has made significant contributions to mitigating real-world threats.We have reported 1,726 phishing addresses to the community, accounting for 42.7% of total community contributions during the same period.Additionally, we have sent 2,539 on-chain alert messages, assisting 1,980 victims.This research serves as a valuable reference in combating the emerging PTXPHISH and safeguarding users' assets.
The rapid development of blockchain technology has led to a constant increase in its financial and technological value. However, this has also led to malicious attacks. Distributed denial-of-service attacks pose a considerable threat to blockchain technology out of many attacks due to its effectiveness and distributed nature. To protect the blockchain from DDoS attacks, researchers have proposed a large number of defensive schemes. However, these schemes are not well-suited for use in practical situations. In this work, we propose a DDoS attack detection scheme based on centralized federated learning, where multiple participating nodes locally train models and upload them to a central node for aggregation. Additionally, we propose a more suitable method for blockchain scenarios, using decentralized federated learning technology, where multiple nodes exchange models in a peer-to-peer manner to complete model training without a central server. We simulate DDoS attacks in blockchain and generate a large dataset by combining it with traditional network layer DDoS attack data to evaluate the effectiveness of our schemes. The experimental results show that the proposed schemes perform well in classification accuracy, demonstrating that our techniques can detect DDoS attacks effectively.
Smart grid technologies have rapidly become one of the largest and most comprehensive sources of data for the modern utility. For the most part, data streams are seen as an essential tool that enable utilities to carry their day-to-day business operations, but they also create the need for efficient and secure data management strategies. In the context of the smart grid, ensuring data privacy is becoming an increasing concern due to a combination of factors that range from shifts in operational paradigms and rapid technology evolution to changes in legislation. Furthermore, researchers have highlighted the risks associated with improperly protected energy records. For example, energy consumption data from homes could be used to infer the behaviors and habits of home occupants through activity recognition or user profiling (Fan, 2017), which may lead to unfair service pricing, targeted advertising, or other personal security violations. Similarly, Electric Vehiclesâ (EVs) charging metadata could be used to reveal private information about the owner such as their payment methods, preferred charging stations, and other locational and timing information that could be used to reconstruct the vehicle ownerâs behaviors. The privacy of user data, even when used for statistical analysis or machine learning training processes, also needs to be carefully considered, as an individualâs private traits may still be vulnerable if their inclusion/exclusion greatly impacts the result or could be linked to a public dataset through cross-reference. The breach of user privacy also has severe impacts for organizations that store, transmit, or work on the data in the form of diminishing the publicâs trust in them while potentially incurring legal consequences (e.g., fines and suspensions under the European Union General Data Protection Regulation, Health Insurance Portability and Accountability Act, etc.). Because of these risks, several privacy-preserving mechanisms are available to help organizations comply with privacy legislations and prevent the unauthorized and malicious use of user data. In light of these concerns, this report focuses on performing a computational review of privacy-preserving mechanisms that have received a significant amount of interest in literature. It specifically focuses on 1) homomorphic encryption, 2) zero-knowledge proofs, 3) differential privacy, and 4) federated learning. It is worth noting that although many of the methods presented in this document rely on cryptographic primitives, their intent is not to provide perfect secrecy, but rather to enable users to maintain privacy, and thus they shall not be compared or equated to other constructs that are aimed to address cybersecurity constructs.
This study introduces an innovative blockchain-based voting system that leverages non-fungible tokens to enhance the integrity, openness, and accessibility of elections. By harnessing the decentralized nature of blockchain and the distinctive characteristics of NFTs, the proposed system aims to address common vulnerabilities in traditional voting methods. The research findings indicate that a blockchain-based voting system utilizing NFTs can substantially improve election integrity. NFTs are employed to authenticate voter identities, bolstering security and mitigating fraudulent activities. Additionally, the public blockchain ledger ensures the permanent recording of votes, promoting transparency and confidence in election outcomes. Furthermore, the internet-enabled voting approach allows participation from any location, reducing barriers to voter engagement and improving accessibility. This paper examines the technical implementation of the proposed system, including the application of smart contracts and cryptographic methods to protect the voting process. It also explores potential obstacles and areas for future investigation, highlighting the transformative potential of this approach in democratic procedures.
Abstract In recent years, the widespread adoption of Ethereum-based transactions, such as cryptocurrencies and blockchain technologies, have revolutionized the way financial transactions are conducted. These decentralized and transparent systems offer numerous advantages, including enhanced security, immutability, and reduced transaction costs. However, alongside their benefits, Ethereum-based transactions have also attracted the attention of malicious actors seeking to exploit unsuspecting users through phishing scams. Phishing scams have thus become frequent in this scenario. Therefore, it is required to implement an effective and reliable phishing scam detection method. In this paper, we present the implementation of a highly efficient detection method by carrying out a graph-like data network formation, over which we then apply models that are based on graph neural networks like Magnet Link Prediction and Graph AutoEncoder Pathfinder Discovery Network Algorithm (GAE_PDNA). This helps in extracting useful information from the nodes of the graph. After relevant embeddings have been obtained, the classification of the phishing account is performed using AdaBoost classifier that helps in complex decision-making and detects the accounts related to the phishing scams. Our best model attains a precision of 0.99 and an F1 score of 0.99. Highlights
Roberto A. Pava-DĂaz, JesĂșs Gil-Ruiz, Danilo Alfonso LĂłpez-Sarmiento
Self-sovereign identity (SSI) embodies the fundamental human right to own and control a digital identity that grants access to public, social, and financial services. The absence of a dedicated digital identity layer in the development of the Internet has rendered SSI a significant challenge in contemporary society. Blockchain technology emerges as a promising solution by enabling the creation of decentralized and automatically verifiable identities. This study contextualizes SSI and analyzes how blockchain technology facilitates the autonomous management of digital identities. It explores nine prominent frameworks in this fieldâSovrin, uPort, Jolocom, ShoCard, Litentry, Civic, KILT, Idena, and IONâhighlighting their features, functionalities, and compliance with digital identity principles. The research concludes by identifying the challenges and opportunities in implementing these systems for digital identity management, thus contributing to the advancement of this emerging field.
Smart grids offer promising opportunities for energy efficiency but raise concerns about data privacy and transparency. This paper presents a new framework for Secure and Auditable Private Data Sharing designed specifically for smart grids, with a focus on its functionality as a password manager. By utilizing smart contracts and blockchain technology, the framework creates explicit guidelines for data usage that guarantee clear oversight of data access and intended uses. In addition to managing smart grid data, the framework securely stores passwords and credentials, providing users with a comprehensive solution for data security and access management. The use of off-chain smart contract execution and trusted execution environments maximizes performance. To further assure transactional reliability during the result dissemination and payment procedures, the framework employs a two-phase atomic delivery mechanism. Energy service providers gain from optimum contracts because they encourage high-quality data exchange and customer participation through the use of contract theory. Extensive simulations validate the efficacy of the proposed approach, enabling users to securely access their data remotely through a cloud platform integrated with open-source services.
Condorcet voting is widely regarded as one of the most important voting systems in social choice theory. However, it has seen little adoption in practice, due to complex tallying and the need to break ties when there is a Condorcet cycle. Several online Condorcet voting systems have been developed to perform digital tallying and tie-breaking procedures, but they require voters to completely trust the server. Additionally, many end-to-end (E2E) verifiable e-voting systems require trustworthy authorities to perform complex decryption and tallying operations. We propose VERICONDOR, the first E2E verifibbolable Condorcet e-voting system without tallying authorities. VERICONDOR allows a voter to fully verify the tallying integrity by themselves while providing strong protection of ballot secrecy. We present novel zero-knowledge proof techniques to prove the well-formedness of an encrypted ballot with exceptional efficiency. VERICONDOR supports ranking candidates with strict preference, as well as indifference. The computational cost is exceptionally efficient for strict preferences at \(\mathcal{O}(n^{2})\) per ballot for \(n\) candidates, while remaining practical for indifferences at \(\mathcal{O}(n^{3})\) . In the case of ties, we show how to apply known Condorcet methods to break them in a publicly verifiable manner. Finally, we present a proof of concept implementation and evaluate its performance.
Abstract High voter turnout in elections and referendums is desirable to ensure a robust democracy. Secure electronic voting is a vision for the future of elections and referendums. Such a system can counteract factors hindering strong voter turnout such as the requirement of physical presence during limited hours at polling stations. However, this vision brings transparency and confidentiality requirements that render the design of such solutions challenging. Specifically, the counting implementation must support reproducibility, and the choice of individual voters must remain confidential. In this paper, we propose and evaluate a novel referendum protocol that ensures transparency, confidentiality, and integrity, in trustless networks. The protocol is built by combining secure multi-party computation and distributed ledger technology, e.g., a Blockchain. The persistence and immutability of the protocol communication allow verifiability of the referendum outcome by any participant. Voters therefore do not need to trust third parties. We provide a formal description and conduct a thorough security evaluation of our proposal.
Muhammad Razali, Azrul Amri Jamal, Syed Abdullah Fadzli, Muhammad D. Zakaria · 6 authors
The act of voting is an inherent and essential entitlement that is universally granted to all individuals. Electronic voting, commonly known as e-voting, is a voting method that utilises electronic equipment to facilitate and manage the process of casting and tallying votes. Electronic voting systems are employed to expedite the process of tallying ballots. Furthermore, it will reduce the amount of money needed to pay for counting staff while also reducing human error. The implementation of remote voting would greatly benefit individuals residing at a considerable distance from their designated polling location, as it would afford them the convenience of casting their vote at any given time and from any geographical area. The utilisation of blockchain technology presents novel opportunities for the creation and advancement of innovative digital services. The implementation of Blockchain-Enabled e-Voting has promise in mitigating instances of election fraud and enhancing voter accessibility. The voting process involved the utilisation of electronic devices, such as computers or smartphones, by those who met the criteria for voter eligibility. This method ensured that the voting process maintained the principle of anonymity. The significance of electronic credibility services has seen substantial development, becoming as a crucial element inside the contemporary information era. This project seeks to implement the objective of constructing an electronic voting system utilising blockchain technology. The two-level architecture ensures secure voting without relying on current (non-blockchain) technologies for redundancy. The blockchain-based voting project is made up of two components that work together to make the whole thing operate. One will be the admin, who will be in charge of creating elections, as well as adding candidates to the smart contract elections. The other type of user is the voter, who can vote for their preferred candidate and have their vote recorded on the blockchain to make it tamper-proof.
Open access
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques