Abdul Khalique Shaikh, Naresh Adhikari, Amril Nazir, Abdul Salam Shah · 6 authors
<ns3:p>Background Ensuring the security and trustworthiness of a digitized and automated electoral process remains a significant challenge in democratic systems. As digital voting systems are increasingly being investigated around the world, ensuring the integrity of the process using robust security measures is of great importance. This paper presents a simplified model to enhance electoral integrity by leveraging Blockchain technology in the context of Oman’s digital voting system. The model uses Blockchain technology to create a secure and trustworthy voting environment, addressing key vulnerabilities in digital electoral systems. Methods The research utilized a quantitative approach, employing an experimental design methodology using open-source software to simulate voting systems. Synthetic population data is utilized for operating these systems, while advanced biometric authentication technologies are used to verify voter identities. Blockchain technology is leveraged to ensure secure vote recording, with smart contracts used to authenticate voters and securely record votes. Additionally, synchronous transactions are executed for both voter registration and voting processes, enhancing the overall security and efficiency of the system. Results The experimental results shows that Blockchain enhances electoral integrity and security in Oman voting system, improves transparency and reliability in elections. The performance evaluation of the model focuses on efficiency, reliability, and scalability metrics. Asynchronous transactions are utilized to improve processing time for voter registration and voting. Election administrators can manage, monitor, and certify election results, while Ethereum nodes ensure decentralized verification and transparency in the voting process. Conclusion This research offers insights for policymakers to consider Blockchain for electoral reforms, addressing issues like data integrity, fraud prevention, and transparency to boost voter trust. A strong regulatory framework and public awareness are crucial for successful implementation. Pilot projects are needed to assess Blockchain’s practical impact. Oman could lead global innovation in electoral technology, though infrastructure and public resistance challenges must be managed.</ns3:p>
Mohammed Shalan, Md Rakibul Hasan, Yan Bai, Juan Li
The increasing adoption of smart home devices has raised significant concerns regarding privacy, security, and vulnerability to cyber threats. This study addresses these challenges by presenting a federated learning framework enhanced with blockchain technology to detect intrusions in smart home environments. The proposed approach combines knowledge distillation and transfer learning to support heterogeneous IoT devices with varying computational capacities, ensuring efficient local training without compromising privacy. Blockchain technology is integrated to provide decentralized, tamper-resistant access control through Role-Based Access Control (RBAC), allowing only authenticated devices to participate in the federated learning process. This combination ensures data confidentiality, system integrity, and trust among devices. This framework’s performance was evaluated using the N-BaIoT dataset, showcasing its ability to detect anomalies caused by botnets such as Mirai and BASHLITE across diverse IoT devices. Results demonstrate significant improvements in intrusion detection accuracy, particularly for resource-constrained devices, while maintaining privacy and adaptability in dynamic smart home environments. These findings highlight the potential of this blockchain-enhanced federated learning system to offer a scalable, robust, and privacy-preserving solution for securing smart homes against evolving threats.
Blockchain-based covert communication enables undetectable data transmission by constructing covert channels in blockchain networks. However, extant approaches require transactions carrying secret data to be permanently preserved in the public ledger, which cannot resist data mining. Besides, these solutions cost up to $48,169 to transmit 1-MegaByte (MB) data. In this paper, we introduce a Tripartite Covert Communication Model (TCCM), which amalgamates blockchain and the Inter Planetary File System (IPFS) to facilitate the transfer of MB-level files while simultaneously circumventing data mining. TCCM comprises an IPFS covert channel, a ledger-layer covert channel, and a network-layer covert channel. The IPFS covert channel transmits the initial secret data. The ledger-layer covert channel embeds a timestamp into the blockchain transaction, which governs the construction time of the network-layer covert channel. The network-layer covert channel conveys the content identifier of the secret data utilizing Bitcoin's inventory message. We further present a TCCM instantiation and formally prove its unobservability. We instant TCCM to evaluate its performance on the Bitcoin mainnet. We also discuss its scalability, real-world use cases, and ethical considerations. Experimental outcomes demonstrate that the proposed instantiation is unobservable and able to transmit 100-MB files at a cost of $1.47.
The medical industry has made significant advancements in recent years. However, the lack of accountability in medical management has resulted in systemic deficiencies, which have adversely affected patient trust and contributed to an increase in medical disputes. As a result, there is a growing emphasis on managing the quality of medical services, particularly in enhancing patient experience. To address these challenges, we propose a new system for evaluating health services. This system will allow patients to anonymously rate the services they receive while also providing doctors the opportunity to appeal specific reviews. The hospital handles the evaluations and appeals through the management of the cloud platform. We propose a new scheme to assist the work of the platform, which is a lattice-based group signature with verifier-local revocation (VLR-GS). Most of the work on VLR-GS has focused on the random oracle model (ROM) or using non-interactive zero-knowledge proofs (NIZKs). Our construction is anonymous and traceable in the standard model under the hardness of the learning with errors problem and short integer solution problem. Furthermore, theoretically analyzing it has practical significance in both security and efficiency. In conclusion, the proposed scheme establishes a secure and privacy-oriented platform for an anonymous medical service evaluation system, with the goal of fostering patient trust and improving hospital service quality within the healthcare sector.
<div> Mixnet protocols are used in electronic voting protocols to mix the ballot box before the tally, to preserve ballots privacy and unlinkabiliy. Whereas proving security properties of the other components of the electronic voting protocols has globally already been done in several logical frameworks and tools, proofs of mixnets remain a real challenge to handle. In this paper we focus on the quite recent CCSA logic, which enables handling of computational security proofs with first-order logics facilities. We enrich the logic to be able to deal with zero-knowledge proofs and rewinding techniques, and provide the first complete proof of Terelius-Wikström mixnet protocol. </div>
Threshold cryptography is a powerful and well-known technique with many applications to systems relying on distributed trust. It has recently emerged also as a solution to challenges in blockchain: frontrunning prevention, managing wallet keys, and generating randomness. This work presents Thetacrypt, a versatile library for integrating many threshold schemes into one codebase. It offers a way to easily build distributed systems using threshold cryptography and is agnostic to their implementation language. The architecture of Thetacrypt supports diverse protocols uniformly. The library currently includes six cryptographic schemes that span ciphers, signatures, and randomness generation. The library additionally contains a flexible adapter to an underlying networking layer that provides peer-to-peer communication and a total-order broadcast channel; the latter can be implemented by distributed ledgers, for instance. Thetacrypt serves as a controlled testbed for evaluating the performance of multiple threshold-cryptographic schemes under consistent conditions, showing how the traditional micro benchmarking approach neglects the distributed nature of the protocols and its relevance when considering system performance.
This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development. High-quality watermarks adhering to the detectability-fidelity-robustness tri-objective are limited due to codes' low-entropy nature. Watermark verification, however, often needs to reveal the signature and requires re-encoding new ones for code reuse, which potentially compromising the system's usability. To overcome these challenges, RoSeMary obtains high-quality watermarks by training the watermark insertion and extraction modules end-to-end to ensure (i) unaltered watermarked code functionality and (ii) enhanced detectability and robustness leveraging pre-trained CodeT5 as the insertion backbone to enlarge the code syntactic and variable rename transformation search space. In the deployment, RoSeMary uses zero-knowledge proofs for secure verification without revealing the underlying signatures. Extensive evaluations demonstrated RoSeMary achieves high detection accuracy while preserving the code functionality. RoSeMary is also robust against attacks and provides efficient secure watermark verification.
Ichsan Nur Ahmad, Parman Sukarno, Aulia Arif Wardana
This research paper presents the development of an electronic voting system that utilizes blockchain technology and Zero Knowledge Proof to enhance privacy and security during voting. Electronic voting systems have many issues regarding privacy, data integrity, and proper transparency, whereas traditional voting systems face the issue of being cost-effective. This research has solved the previously mentioned issues by using ZKP over the polygon network. Furthermore, ZKP, along with blockchain technology, helps in the decen-tralization, immutability, and transparency of the stored data while maintaining the confidentiality of the voter identity in the process of authentication and vote validation. Moreover, cryptographic verification is aided through role management and a user interface, which results in a quicker verification process than traditional methods. Using a polygon network as the medium for conducting tests has shown a low transaction cost of 0.010 POL to facilitate voting while providing substantial privacy. The approach presented in this research has significantly improved over traditional E-voting systems in terms of cost, security, and scalability, indicating that this approach is better suited for election processes today.
The work examines modern approaches to building electronic voting systems, such as blockchain, which promises to revolutionize the process due to its immutability and decen-tralization properties, as well as traditional cryptographic methods, including homomorphic encryption, which allows vote counting without the need to decrypt each individual vote. Blind signatures ensure the ability to confirm a vote without disclosing the user's identity, and zero-knowledge proofs allow voting without interacting with the server. The goal of the work is to select an approach for building electronic voting systems based on a comparative analy-sis of their key characteristics. The solved tasks include reviewing the requirements, general-ized structures, and main procedures of electronic voting systems; analyzing the existing types of electronic voting systems and their comparative characteristics. During the work, existing systems and other literature were thoroughly analyzed. The article provides a de-tailed analysis of the advantages and limitations of these technologies, as well as their suit-ability for different electoral systems, considering important aspects such as scalability, effi-ciency, and protection against potential threats. Throughout the work, a list of requirements for electronic voting systems was compiled, the main procedures present in electronic voting systems were outlined, a set of actors in typical electronic voting systems was defined, and the generalized structures of their main types were presented. A comparative analysis of the types of electronic voting systems based on compliance with the requirements was conducted. An approach was chosen for further system development.
Satellite communication (SC) is an indispensable component of future communication systems due to its extensive coverage and flexibility. In this work, we investigate the fine-grained anonymous access technique, which not only ensures user equipments (UEs) privacy in the open SC environment but also enables satellites to enforce fine-grained access policies with low overhead. Firstly, we design a Merkle forest based framework that facilitates efficient UE attribute data synchronization, leveraging the broadcasting capability of satellites. Subsequently, we construct a zero-knowledge based protocol for UE authentication, key agreement, and handover in SC. The analysis demonstrates the protocol's resilience against prevalent attacks, and simulation results validate its practicality.
J. John Shiny, K. Penyameen, Raj Shree, M. Hannah Nissi · 5 authors
Democratic countries have to ensure electoral integrity so that the public can retain their confidence in voting. Current electronic systems now in use are characterized by many problems, among them being manipulation caused by centralizing control. In fact, they violate voter anonymity, equity, and transparency. Traditional procedures in voting have flaws, be it paper-based or computer-based, which make it prone to manipulation and fraud. To such an end, blockchain technology, emerging as a decentralized alternative, provides end-to-end verification, transparency, and security in electronic voting systems. The paper describes how the blockchain electronic voting system employs a decentralized network to eliminate dangers associated with centralized authorities, addressing, fundamentally, inherent flaws in conventional approaches. The proposed system deploys smart contracts for a vote process that would be proof against tampering, transparent, and secure in the voting processes while ensuring voter anonymity by concealing their personal information and minimizing the possibility of illegal voting and data breaches. In addition, it is most likely to provide cost-effective operations with increased voter turnout as an election can be conducted online without tied-down rigidity. The study demonstrates how blockchain can effectively be used to make the legitimacy and security of electronic voting systems far better than the current voting systems and thereby help restore confidence in the democratic process.
N. Anitha, S. Das, Shree Ganesh K S, R Shrilakshmi
There is an enormous need for a secure and efficient online decentralized voting system. This paper mainly revolves around our proposed model for the same, that leverages the robustness of Blockchain Ethereum Technology and the precision of machine learning algorithms for user authentication. The system ensures a transparent, tamper-proof, and user-friendly voting process, enabling users to participate in elections from remote locations and eliminating the possibility of under-the-bench corruption. The fusion of a facial recognition system, built on a python-based Django-powered backend, authenticates users before voting. Upon successful authentication, voters receive their ballots via email, which directs the voters to a React-based web application for casting votes. This paper outlines the system architecture, the flow of events, the technologies utilized, and the implementation specifics, highlighting the innovation and security attributes that distinguish this methodology as a viable alternative to traditional voting methods.
Abstract— Remote electronic voting promises increased accessibility but remains constrained by persistent challenges related to coercion in unsupervised environments, credential compromise, and the difficulty of sustaining long-term voter trust. While coercion-resistant approaches commonly rely on revoting, most existing systems treat credential loss or recovery as an administrative exception, often reintroducing identity linkage or trusted intermediaries and offering limited means for voters or observers to verify that an election unfolded as intended. This paper presents Arcaunt, a remote voting architecture that elevates anonymous credential recovery to a first-class security property and integrates it directly into the voting lifecycle. The architecture introduces an Anonymous Recovery Channel (ARC), enabling voters to revoke and replace compromised credentials without identity disclosure or reliance on administrator discretion. Recovery is logically and operationally decoupled from ballot casting. This preserves ballot secrecy and prevents temporary compromise of credentials, devices, or voter autonomy from becoming a permanent loss of voting control. Arcaunt builds on established cryptographic mechanisms, including publicly verifiable bulletin boards, commitment-based ballots, and unlinkable bearer credentials. These components provide ballot integrity and verifiability without exposing voter identities and form the foundation on which revoting, recovery, and auditability are composed. Individual assurance is provided through deferred, non-transferable verification mechanisms: voters receive a receipt at ballot submission, while verification becomes possible only after election closure, preventing real-time feedback that could enable coercion while still allowing voters to confirm that their final valid ballot was recorded and included. At the system level, integrity is enforced through an append-only, publicly auditable ledger and deterministic “last valid vote” counting rules, ensuring that administrative database access cannot alter election outcomes without detection. The architecture explicitly bounds its threat model, acknowledging limits against global traffic analysis and continuous coercion while constraining failures to be temporary and non-scalable. We analyze the security properties of the proposed system under realistic adversarial assumptions and evaluate a prototype implementation, demonstrating that anonymous recovery, coercion-resistant revoting, individual verification, and public auditability can be combined efficiently without reliance on trusted administrators or specialized hardware. Keywords—e-voting, arcaunt, anonymous recovery channel (arc), coercion resistance, sha-3, digital democracy, govtech, zero-knowledge proofs.
Consumer electronics devices (CEDs) are becoming increasingly interconnected and integrated into our daily lives. Thus, the demand for seamless communication, enhanced security, and reliable performance has increased. However, the widespread adoption of these devices raises significant concerns regarding security and privacy in computing, especially when collecting and processing sensitive consumer data. To address these challenges, robust security mechanisms are necessary to protect this sensitive data. In response to these challenges, Blockchain-Enabled Federated Learning for Consumer Electronics Devices (BFLCED) is proposed to make CEDs more secure and privacy-preserving. The combination of blockchain and federated learning (FL) provides a robust solution for real-world CEDs where data privacy and security are most important. The proposed BFLCED ensures devices are authenticated and communicated securely to maintain data integrity and confidentiality during model training. It generates unique identities using the Lightweight Elliptic Curve Digital Signature Algorithm (LECDSA) and digital signatures for data integrity. Parallelly, smart contracts are employed to verify device identities & data integrity automatically and enable secure communication among devices. Data privacy is maintained during model aggregation by securely aggregating updates using encryption and multi-party computation (MPC). In the end, a security analysis is conducted to evaluate the effectiveness of the proposed mechanisms in safeguarding CEDs against potential threats and vulnerabilities. Furthermore, the proposed BFLCED transforms automation and personalization by securely connecting CEDs to our daily lives.
R. Vijay Anand, G. Magesh, I. Alagiri, Madala Guru Brahmam · 9 authors
With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includes some such issues as follows: data privacy, centralized vulnerability, inefficiency in fraud detection and much more. To that effect, and to address such limitations, this paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency throughout the network of transactions. We begin with a design framework based on Federated Learning for Blockchain Integration where distributed datasets across blockchain nodes contribute to a global machine learning model but do not share raw data samples. Different nodes learn their own models. After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. With LSTMs Autoencoders, more excellent security protocols are created for anomaly detection and fraud. So, by training the autoencoder on normal transaction data, the system can alert transactions with high reconstruction errors, meaning real-time anomalies. This proactive detection of anomalies reduces fraudulent activities significantly as most of the threats are recognized early. To this end, this paper proposes Smart Contract-based Model Management for machine learning models in a decentralized environment. Smart contracts are responsible for the submission, validation, and execution of the locally updated models in a decentralized fashion such that the management process is transparent and tamper resistant. Integrity and authenticity requirements are fulfilled by enforcing consensus mechanisms. Privacy in Machine Learning is guaranteed through Differential Privacy and Homomorphic Encryption. Differential privacy techniques, so as to ensure individual transaction data privacy in the updates of the local model before aggregation. In homomorphic encryption, computations are made in the encrypted form so when forming privacy preserving global model, privacy is preserved. The Real-time analysis of the transactions can be done with CNNs to detect fraud. Streaming transaction data is analyzed by CNNs leveraging the privacy-preserving global model and producing immediate alerts and actions for detected fraud. This real timing makes the network even more reliable and trustworthy. Our proposed framework is effective according to the interim outcomes where the aggregation of local models occurred without data leakage, detected anomalies very efficiently, managed models very transparently, with privacy of data at a very high level, and easily detected fraudulent transactions. The work presented here provides a great boost to send secure and very easily transparent transactions across the network, and thus resulted in enhanced network trust and decentralization.
Open access
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Cryptocurrency such as Bitcoin supports anonymous routing (Tor and I2P) because of the application requirements of anonymity and censorship resistance. In permissionless and open networking for cryptocurrency, an adversary can spoof to pretend to use Tor or I2P for anonymity and privacy protection, while in reality it is not using anonymous routing and forwarding its networking directly to the destination peer to reduce the networking overheads. Using profile detection to detect anonymous routing and false claims based on the deterministic features are vulnerable to spoofing, especially in the permissionless cryptocurrency bypassing registration control. We therefore design and build network fingerprinting using the networking behaviors to detect and classify the networking types. We build a network sensor to collect data on an active Bitcoin node connected to the Mainnet and apply supervised machine learning to classify if a peer node is using IP (not anonymous), Tor, or I2P. Our results show that our scheme is effective in accurately detecting the networking types and identifying spoofing attempts through supervised machine learning. Our machine learning model accurately classifies the networking types and detects fake claims of Tor usage with 90% accuracy and false claims of I2P with 87% accuracy in permisionless Bitcoin.
Internet Traffic Analysis and Secure E-voting
Network Security and Intrusion Detection
Advanced Steganography and Watermarking Techniques
This paper explores a blockchain-based e-voting system aimed at addressing traditional voting challenges, such as vote tampering, delayed results, and privacy issues. The proposed framework leverages Merkle tree structures for secure, efficient data verification and blockchain's distributed ledger to ensure immutability and transparency.
R. Lalitha, Singam Veera Shankarreddy, V Dharani, K. Dhanalakshmi · 5 authors
In the era of digital transformation, trust and security are paramount in ensuring the integrity of various online transactions and processes, including voting systems. Traditional centralized trust management and voting systems have faced challenges related to security, transparency, and accountability. To address these issues, this research introduces a novel Blockchain-Enabled Decentralized Trust Management and Secure Voting System. It leverages blockchain technology, a decentralized and immutable ledger, to enhance trust, transparency, and security in trust management and voting processes. This system combines several key components, including smart contracts, cryptographic techniques, and distributed ledger technology, to create a robust and tamper-resistant infrastructure. In the trust management aspect, the proposed system employs a decentralized reputation system where individuals and entities can build trust through transparent and verifiable interactions. Smart contracts automate trust-building processes and provide a reliable mechanism for dispute resolution. This system allows for trust to be quantified and established in a trustless environment. The secure voting component introduces a tamper-proof and transparent voting system. Through the use of cryptographic keys and digital signatures, voters can securely cast their votes while ensuring anonymity and integrity. All voting transactions are recorded on the blockchain, providing a permanent and auditable record of the election process. Verification of election results becomes accessible to all stakeholders, enhancing transparency and trust in the electoral process. The Blockchain-Enabled Decentralized Trust Management and Secure Voting System represents a significant step towards enhancing trust and security in critical online processes, such as voting. By combining blockchain technology, cryptography, and decentralized trust mechanisms, it offers a promising solution to address the challenges associated with centralized trust management and voting systems. This research paves the way for a more transparent, secure, and accountable future in the realm of digital trust and elections.
The cross-chain identity authentication method based on relay chains provides a promising solution to the issues brought by the centralized notary mechanism. Nonetheless, it continues to encounter numerous challenges regarding data privacy, security, and issues of heterogeneity. For example, there is a concern regarding the protection of identity information during the cross-chain authentication process, and the incompatibility of cryptographic components across different blockchains during cross-chain transactions. We design and propose a cross-chain identity privacy protection method based on relay chains to address these issues. In this method, the decentralized nature of relay chains ensures that the cross-chain authentication process is not subject to subjective manipulation, guaranteeing the authenticity and reliability of the data. Regarding the compatibility issue, we unify the user keys according to the identity manager organization, storing them on the relay chain and eliminating the need for users to configure identical key systems. Additionally, to comply with General Data Protection Regulation (GDPR) principles, we store the user keys from the relay chain in distributed servers using the InterPlanetary File System (IPFS). To address privacy concerns, we enable pseudonym updates based on the user’s public key during cross-chain transactions. This method ensures full compatibility while protecting user privacy. Moreover, we introduce Zero-Knowledge Proof (ZKP) technology, ensuring that audit nodes cannot trace the user’s identity information with malicious intent. Our method offers compatibility while ensuring unlinkability and anonymity through thorough security analysis. More importantly, comparative analysis and experimental results show that our proposed method achieves lower computational cost, reduced storage cost, lower latency, and higher throughput. Therefore, our method demonstrates superior security and performance in cross-chain privacy protection.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques