Blockchain-based digital voting systems have emerged as a promising solution to enhance the security, transparency, and accessibility of voting processes. By leveraging distributed ledger technology, these systems aim to mitigate various challenges associated with traditional voting methods, such as fraud, manipulation, and logistical complexities. However, the adoption of blockchain in voting introduces both security and usability considerations that must be carefully evaluated. In this paper, we conduct a comprehensive analysis of blockchain-based digital voting systems, focusing on their security and usability aspects. We examine the underlying cryptographic mechanisms, consensus protocols, and smart contract implementations to assess their resilience against potential attacks and vulnerabilities. Additionally, we investigate the user experience, accessibility, and scalability of these systems to evaluate their usability in real-world voting scenarios. Through this analysis, we aim to provide insights into the strengths, limitations, and trade-offs associated with blockchain-based digital voting systems, facilitating informed decision-making and further research in the field of secure and user-friendly electronic voting technologies.
The integrity, transparency, and security of voting systems are crucial to maintaining the democratic process. Traditional electronic voting systems have faced several challenges, including vulnerabilities to hacking, fraud, and tampering. Blockchain technology, known for its decentralized and immutable nature, has emerged as a potential solution to address these issues. This paper explores the application of blockchain-based solutions in creating secure and transparent voting systems. By leveraging the distributed ledger technology of blockchain, the proposed systems ensure data integrity, confidentiality, and voter authentication while enabling real-time auditing. Blockchain-based voting systems offer several advantages, including resistance to vote tampering, the prevention of double voting, and enhanced accessibility for remote and disabled voters. Moreover, the use of cryptographic techniques and smart contracts further enhances security and transparency, allowing for verifiable, auditable, and tamper-proof elections. This review highlights existing research and prototypes, discusses the challenges of implementing such systems, and provides future directions for the development of blockchain-enabled electoral solutions.
Integrity and transparency in electoral procedures are essential for the actual functioning of democratic countries. The present voting systems often face numerous issues such as vote rigging, counterfeit ballots, lack of transparency, and inefficiencies. Blockchain-enabled voting systems are promising, but they face challenges in maintaining public trust due to technical concerns, such as transparency, security, privacy, and scalability. The architecture of a Hyperledger-based framework is proposed to design and construct a robust and secure prototype for a blockchain-enabled voting system. Effective algorithms for key electoral processes such as identity management for voter authentication, vote casting, vote counting and vote tallying, using multi-signature validation are deployed. Contemporary cryptographic techniques, such as zero-knowledge proofs, homographic encryption, and digital signatures, ensure that votes are encrypted and anonymized, protecting voter privacy and facilitating a verifiable election process. Through an exploratory work, the recommended prototype using Hyperledger Fabric is compared with conventional electoral systems based on key parameters. This study demonstrates that a blockchain-based election system is able to maintain the integrity and efficiency of state-of-the-art technology by recommending a robust and secure prototype for conducting transparent and verifiable elections.
Urooj Waheed, Sadiq Ali Khan, Muhammad I. Masud, Huma Jamshed · 6 authors
The adoption of the Internet of Things (IoT) in smart household energy systems offers new opportunities for efficiency and automation, while also posing substantial security challenges. These systems utilize diverse standards and protocols to autonomously access, collect, and share energy-related data over distributed networks. However, this interconnectivity increases their vulnerability to cyber threats, making the system vulnerable to cyber threats. The literature reveals numerous cases of cyberattacks on IoT-based energy infrastructures, primarily involving unauthorized access, data breaches, and device exploitation. Therefore, designing a robust ecosystem with secure and efficient access control (AC), while safeguarding user functionality and privacy, is essential. This paper proposes a dynamic attribute-based access control (ABAC) model that leverages a hybrid blockchain architecture to enhance security and trust in smart household energy systems. The proposed architecture integrates Hyperledger Fabric for managing user, resource, and device attributes using smart contracts, while Hyperledger Besu enforces decentralized access policies. Additionally, a trust recalibration mechanism dynamically adjusts access permissions based on behavioral analysis, mitigating unauthorized access risks and improving energy system adaptability. Experimental results demonstrate the model’s effectiveness in securing IoT smart home energy, while ensuring seamless device onboarding and efficient access control.
An E-voting framework utilizing decentralized technology can establish a secure and transparent environment for elections, where voters can confidently cast their ballots knowing that their votes are final and untampered with. Blockchain's decentralized structure ensures that votes are recorded accurately, preventing interference from external actors. In a protected Evoting framework, each vote becomes part of an immutable, distributed ledger, allowing for peer-to-peer validation of transactions. This ensures that each voice counts as the only, unchanging record. The results can be reported immediately as soon as the voting process is completed. Voting is a critical process carried out in democratic societies, usually through secret voting documents or other similar methods. However, traditional voting systems are often plagued by problems such as voting manipulation, low turnout and logistics challenge. To solve these problems, we propose implementation of decentralized voting platforms that offer advanced security, efficiency and confidence in the election process
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Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The proliferation of numerous portable mobile devices has made mobile crowd-sensing (MCS) systems a promising new trend. Traditional MCS systems typically outsource sensing tasks to the data aggregator (e.g., cloud server). They collect and analyze the provided sensing data through an appropriate truth discovery (TD) method to identify valuable data sets. However, existing privacy-preserving MCS systems lack transparency, enabling data aggregators to deviate from the specified protocols and allowing malicious users to provide false or invalid sensing data, thereby contaminating the resulting data sets. The lack of transparency and public verifiability in MCS systems undermines widespread adoption by preventing data requesters from confidently verifying data integrity and accuracy. To address this issue, we propose a transparent and privacy-preserving mobile crowd-sensing system with truth discovery (TP-MCS) constructed using zero-knowledge proof (ZKP) and the Merkle commitment tree. This scheme enables data requesters to effectively verify the correctness of the truth discovery service while ensuring data privacy. Furthermore, theoretical analysis and extensive experiments demonstrate that this scheme is secure and efficient.
Following the emergence of the COVID-19 pandemic, electronic voting has gradually become an inseparable part of people's lives. However, it has also raised a series of severe privacy and trust challenges. The immutable and publicly transparent characteristics of blockchain are a perfect fit for the development of electronic voting systems, effectively eliminating voters' concerns about ballot tampering.At the same time, zero-knowledge proofs enable the prover to show they possess certain information to the verifier, without disclosing the actual details. It is important to note that with the rapid development of quantum technology, traditional cryptographic schemes face unprecedented security threats. To address this challenge, We present a quantum-resistant blockchain solution for electronic voting, incorporating zero-knowledge proofs. Compared to conventional elliptic curve-based zero-knowledge proof schemes, our proposed solution is based on RLWE, ensuring voter privacy, and uses BFV fully homomorphic encryption technology to implement a blockchain-based electronic voting protocol, ensuring the system’s high availability, security, and anonymous voting. Security analysis and performance testing, along with comparisons to existing similar solutions, show that our scheme has advantages in terms of security and robustness, making it highly practical.
M. K. Ghosh, Chirag Dinesh Jain, Raju Halder, Joydeep Chandra
Phishing scams on the Ethereum network have become a serious threat, especially with the influx of new users into the cryptocurrency market. Current detection methods are mainly focused on long-term consistent transaction patterns with smooth temporal dynamics. However, these methods often struggle to differentiate between phishing and non-phishing users, whose behaviours may appear deceptively similar. Additionally, they face challenges such as network sparsity and data leakage, leading to significant performance limitations. To address these issues, we introduce TEMPER, a novel sequential learning framework designed to jointly capture the subtle distinctions between long- and short-term user behaviours and their correlations to provide more comprehensive insights. TEMPER effectively generates distinguishable user embeddings, enabling the accurate identification of phishing users. Unlike previous approaches, TEMPER mitigates data leakage through a novel sequential transaction sampling algorithm and addresses network sparsity with short-term temporal learning. Through extensive experimentation on three real-world Ethereum datasets, TEMPER demonstrates its efficacy by achieving a 3-4% improvement in the F1-Score compared to existing baseline models, representing a significant advancement in Ethereum phishing user detection.
Mrs. P Maraeswari, Navya Sri Vangala, Anu Chandana Chiluuri, Mohammad Sameer · 6 authors
Abstract: The default voting procedure has many inefficiencies such as issues with effectiveness, security, and transparency. These problems erode the confidence and credibility in the electoral frameworks which fosters conflict and skepticism towards the legitimacy of governance. A solution for voting problems is Secure Sphere, a decentralized ballot system that employs the Ethereum blockchain. Through block technology, Secure Sphere guarantees that its voting process is utterly transparent, secure, and un hackable. Votes are protected against unauthorized additions by casting them on the Ethereum blockchain. This approach mitigates most problems associated with traditional voting systems such as vote tampering and recounting, misrepresentation, and cyber threats. Moreover, the voting process is further secured by the application of cryptographic techniques. The principal feature of Secure Sphere is smart contracts which are vital in automating the voting process. Each vote is verifiable and counted, therefore, once cast, a vote becomes irrevocable. Because of these contracts the system is enhanced to enable real time vote verification, thus rendering the votes straightforwardly auditable. Therefore, both voters and election officials are able to independently confirm the outcomes.
In Ethereum, private transactions, a specialized transaction type employed to evade public Peer-to-Peer (P2P) network broadcasting, remain largely unexplored, particularly in the context of the transition from Proof-of-Work (PoW) to Proof-of-Stake (PoS) consensus mechanisms. To address this gap, we investigate the transaction characteristics, (un)intended usages, and monetary impacts by analyzing large-scale datasets comprising 14,810,392 private transactions within a 15.5-month PoW dataset and 30,062,232 private transactions within a 15.5-month PoS dataset. While originally designed for security purposes, we find that private transactions predominantly serve three distinct functions in both PoW and PoS Ethereum: extracting Maximum Extractable Value (MEV), facilitating monetary transfers to distribute mining rewards, and interacting with popular Decentralized Finance (DeFi) applications. Furthermore, we find that private transactions are utilized in DeFi attacks to circumvent surveillance by white hat monitors, with an increased prevalence observed in PoS Ethereum compared to PoW Ethereum. Additionally, in PoS Ethereum, there is a subtle uptick in the role of private transactions for MEV extraction. This shift could be attributed to the decrease in transaction costs. However, this reduction in transaction cost and the cancellation of block rewards result in a significant decrease in mining profits for block creators.
In response to the issues of high transaction transparency and regulatory difficulties in blockchain account-model transactions, this paper presents a supervised blockchain anonymous transaction model based on certificateless signcryption aimed at ensuring secure blockchain transactions while minimizing both computational and communication overhead. During the transaction process, this approach utilizes certificateless public key signcryption without bilinear pairs to generate anonymous user identities, achieving strong anonymity of user identities and confidentiality of transaction amounts. It employs the Paillier homomorphic encryption algorithm to update transaction amounts and uses the FO commitment-based zero-knowledge proof scheme to validate transaction legality. Additionally, adopting a publicly verifiable secret threshold sharing scheme for hierarchical regulatory authority reduces the security risk of a single regulator storing the regulatory key. This model not only meets the privacy and timely update requirements of account-based blockchain transactions but also effectively regulates abnormal transactions. Rigorous security analysis and proofs demonstrate that this model possesses excellent anonymity, traceability, forward security, and backward security. When compared to similar schemes, the computational cost is reduced by at least 33.18%, effectively fulfilling the requirements for security.
Kode Lakshmi Durga Sindhujasri, Kaduputla Manogna, Sutrayeth Hari Yuktha Nanda, Baligiri Thandava Krishna · 5 authors
Abstract: Elections play a fundamental role in any democratic system, and ensuring their integrity is of utmost importance. Traditional voting methods, such as paper ballots and Electronic Voting Machines (EVMs), suffer from various limitations, including security vulnerabilities, vote tampering, low voter turnout, delays in result processing, and a lack of transparency. Digital voting solutions offer convenience but raise concerns regarding data security and susceptibility to cyber threats. Blockchain technology presents a promising solution to these challenges by providing a decentralized, transparent, and tamperproof framework for conducting elections. As a distributed ledger system, blockchain records transactions in an immutable and verifiable manner, ensuring the integrity of votes. Key features such as decentralization, cryptographic security, transparency, and anonymity make blockchain a robust choice for secure e-voting. In this paper, we propose and implement a blockchainbased e-voting system using Ethereum smart contracts and Web3.js. Our system enforces single-use voting credentials, preventing duplicate votes, and leverages gas fees to mitigate fraudulent voting attempts. Additionally, we develop a web-based application that demonstrates the practical implementation of blockchain voting, discussing its advantages, challenges, and limitations in real-world scenarios
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Dong Liu, Juan S. Giraldo, Peter Pálenský, Pedro P. Vergara
Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). However, training ANNs require vast amounts of SM data, posing privacy risks for households in distribution networks. To ensure customers' privacy during the SM data gathering and online sharing, we introduce a privacy preserving PF calculation framework, composed of two local strategies: a local randomisation strategy (LRS) and a local zero-knowledge proof (ZKP)-based data collection strategy. First, the LRS is used to achieve irreversible transformation and robust privacy protection for active and reactive power data, thereby ensuring that personal data remains confidential. Subsequently, the ZKP-based data collecting strategy is adopted to securely gather the training dataset for the ANN, enabling SMs to interact with the distribution system operator without revealing the actual voltage magnitude. Moreover, to mitigate the accuracy loss induced by the seasonal variations in load profiles, an incremental learning strategy is incorporated into the online application. The results across three datasets with varying measurement errors demonstrate that the proposed framework efficiently collects one month of SM data within one hour. Furthermore, it robustly maintains mean errors of 0.005 p.u. and 0.014 p.u. under multiple measurement errors and seasonal variations in load profiles, respectively.
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.
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.
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.
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.
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
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.
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