Blockchain Papers

Nicolin Decker

No abstract is available for this record.

Hanh Tran Thi, Nghi Nguyen Van, Ngoc Le Anh, Hung Dinh Van

No abstract is available for this record.

Jeremies Enmanuel Chinchay Camargo, Massiel Fiorella Parvina Huaman, Carmen Luz Cuba Cornejo, Cesar Augusto Cabrera Garcia

Digital electoral security has become fundamental to the development of reliable, integrated and available technological systems, driven by the growing demand for transparency and protection against threats. The purpose of this study is to analyze the impact of cryptographic protocols on the security of electoral processes, evaluating their effectiveness against traditional methods. For this purpose, a systematic review of the literature was carried out, considering 50 articles extracted from the Scopus database. The analysis focused on cryptographic techniques applied to blockchain-based environments, such as homomorphic encryption, zero-knowledge proofs and smart contracts, evaluating their contribution to design more secure, auditable and reliable voting systems. The results show that these protocols contribute to prevent recurring vulnerabilities, such as vote tampering, electoral fraud, impersonation and lack of validation, in addition to strengthening auditability and operational reliabilityFinally, the study concludes that the adoption and assessment of cryptographic protocols are essential to reduce risks in electronic voting, and promote more secure, transparent and efficient electoral processes.

Ralph Vince

No abstract is available for this record.

Tuan-Dung Tran, Khoi Le Quoc

No abstract is available for this record.

Md. Jueal Mia, M. Hadi Amini

Federated Learning (FL) is a decentralized learning method that enables collaborative model training while preserving data privacy. This makes FL a promising solution in various applications, particularly in cross-silo settings such as healthcare, finance, and transportation. However, FL remains highly vulnerable to adversarial threats, especially backdoor attacks, where malicious clients inject poisoned data to manipulate global model behavior. Existing outlier detection techniques often struggle to effectively isolate such adversarial updates, compromising model integrity. To address this challenge, we propose Backdoor Attack Resilient Technique for Federated Learning (BART-FL), a novel lightweight defense mechanism that enhances FL security through malicious client filtering. Our method integrates Principal Component Analysis (PCA) for dimensionality reduction with cosine similarity for measuring pairwise distances between model updates andK-means clustering for detecting potentially malicious clients. To reliably identify the benign cluster, we introduce a multi-metric statistical voting mechanism based on point-level mean, median absolute deviation (MAD), and cluster-level mean. This approach strengthens model resilience against adversarial manipulations by identifying and filtering malicious updates before aggregation, thereby preserving the integrity of the global model. Experimental evaluations conducted on the LISA traffic light dataset, CIFAR-10, and CIFAR-100 demonstrate the effectiveness of BART-FL in maintaining model performance across diverse FL settings. Additionally, we perform a comparative analysis against existing backdoor defense techniques, highlighting BART-FL’s ability to improve security while ensuring computational efficiency. Our results showcase the potential of BART-FL as a scalable and adversary-resilient defense mechanism for secure training in cross-silo FL applications.

Itay Tsabary, Alex Manuskin, Roi Bar-Zur, Ittay Eyal

No abstract is available for this record.

Mehrab Najafian

Sudoku tables as a special class of Latin squares are studied and a construction for each class of Sudoku tables is introduced. We provide a definition of a Sudoku table of size m as an m×m table with blocks of size s×d. Twin Sudoku tables of size m with(s, d)-subtables, where s is a divisor of d, are defined as Sudoku tables with (s × d)-subtables and (d×s)-subtables, simultaneously. Solid Sudoku cubes of size m, which are cubes with subcubes of size s×s×d, where s ≤ d and s·d = m, can be divided into subcubes in any face of the Sudoku cube. They have the property that each layer of the cube from each face, which is a table of size m, is a twin Sudoku table. Standard solid Sudoku cubes and 3D Sudoku puzzles are defined as solid cubes in which the subcubes of size m contain m different numbers, and they can be divided into subcubes of size x×y×z along the X, Y , and Z axes, respectively. Magic rectangles and nested magic rectangles are constructed. In addition, orthogonal balanced magic Sudoku tables as a class of Sudoku tables in which their blocks are magic are created and a method to construct nested Latin squares is presented. New classes of Sudoku puzzles are obtained from the construction of the Sudoku tables and Sudoku cubes. Since a Sudoku puzzle can be obtained from a Sudoku table by randomly removing some entries of the Sudoku table, creating a new class of Sudoku tables leads to a new class of Sudoku puzzles. To construct these new classes of Sudoku tables cyclotomic cosets of the algebraic group Zn are used. Sudoku tables have applications in different fields of study, and some of these applications are investigated. It is shown that they can be used in cryptography and zero-knowledge proof protocols. Sudoku puzzles are in the class of NP-complete problems, so zero knowledge proof protocols can be based on this class of problems. Sudoku tables like Latin squares can be used to generate locally repairable codes which have application in distributed storage systems.

Peng Jiang, Liehuang Zhu

No abstract is available for this record.

Mohuya Chakraborty, Sudip Kumar Palit

No abstract is available for this record.

Haixing Li, Yutong Zhou, Chi Zhang, Lingbo Wei

No abstract is available for this record.

Charlotte ; https://orcid.org/0000-0003-2027-5549 Hoffmann, Krzysztof Z ; https://orcid.org/0000-0002-9139-1654 Pietrzak

No abstract is available for this record.

Hongguo Zhang, Yun-Ming Sun, Kaiqi Zhang, Zhibo Guan · 6 authors

With the rapid expansion of digital asset trading, the contradiction between data sharing and privacy protection has increasingly become a significant challenge in the Internet environment. To address this issue, this paper proposes a secure multi-party computation scheme based on blockchain technology. Firstly, in response to the risk of data leakage in distributed storage scenarios, a threshold-based encryption algorithm is designed, utilizing a distributed key protection mechanism to effectively prevent single-point failures and data breaches. Secondly, a smart contract system is developed: the ERC721 contract is used to confirm the ownership of data assets, the ERC20 contract facilitates the transfer of usage rights, and the threshold decryption contract ensures secure multi-party computation and compliant incentive distribution. The collaboration of these three types of contracts enables comprehensive on-chain management of data assets, covering the entire process from ownership confirmation and circulation to compliant usage. In addition, this paper integrates non-interactive zero-knowledge proofs into the multi-party interaction process, allowing public verification of data consistency and computational validity on the blockchain. Finally, experiments are conducted to evaluate the impact of computation latency, communication overhead, and encryption parameters on system performance. The proposed scheme demonstrates significant performance improvements over mainstream SMPC protocols, with a 95.4 % reduction in key generation time and a 19.5 % reduction in ciphertext decryption time. Meanwhile, the scheme effectively resists various semi-malicious attacks, ensuring data security and privacy. • A t-out-of-N threshold ElGamal-based MPC scheme is proposed for secure computation in synchronous environments. • A blockchain smart contract framework manages data assets' lifecycle by combining ERC721/ERC20 and threshold decryption. • A method verifies on-chain data consistency and computation validity using non-interactive zero-knowledge proofs.

Surendra Kumar, Mridula Dwivedi, Mohit Kumar, Huaming Wu · 5 authors

Cyber-physical-social (CPS) systems require searchable encryption (SE) to safeguard sensitive data before storing it in the cloud. Existing dynamic searchable symmetric encryption (DSSE) methods have problems with index creation, document updates that lose data, and search speed. These issues must be addressed to develop an efficient CPS system. Therefore, we have developed a new approach that integrates a DSSE protocol with a blockchain-based data management system, thereby establishing a secure and efficient method for managing encrypted data. We make sure that past data remains private and that we can quickly search through data by building a forward index with a special pseudorandom function (PRF) and checking it with symmetric encryption. Keeping the encrypted index on a private distributed ledger and the secret data on a public ledger reduces storage and speeds up transaction processing. To enhance data privacy and access verification, an additional authentication system must prevent unauthorized access to the private blockchain. Authorization systems verify access permissions and execute the outcomes. Performance evaluation shows that the proposed solution improves the integrity of encrypted data and the speed of queries in the Chicago Crime and Enron Email datasets. The proposed method used only 0.68 MB client and 121.4 MB servers, builds in 121.4 s, updates in 156.4 for client and 167.2 ms for server, and outperforms all in speed, storage, and efficiency. The results are also checked for correctness and originality at the same time to reduce the complexity and computational cost by 60% and 70%, respectively, for modern cyber-physical social applications. Finally, the proposed method improves existing methods based on an extensive evaluation of Chicago Crime and Enron Email datasets, and can benefit modern CPS systems.

Longyang Yi, Jian Liu, Zhiguo Wan, Kui Ren · 5 authors

The recent popularity of cryptocurrencies like Bitcoin and Ethereum has drawn widespread attention to the blockchain technique. In particular, some private cryptocurrencies like Zerocash and Monero enhance privacy protection by concealing the identities of participants and transaction amounts. However, such comprehensive privacy measures present regulatory challenges to malicious activities like money laundering and extortion. Therefore, building a novel blockchain that maintains privacy while supporting regulatory oversight is crucial. In this paper, we propose a regulatable and privacy-preserving blockchain scheme that introduces a decoupled and preparatory regulatory process. It serves as a privacy-preserving first line of defense, enabling the identification of anomalous transactions without compromising the confidentiality of the underlying data. Our approach pioneers a method for anomaly screening on private transactions, mitigating risks without resorting to key escrow or content recovery, thus preserving end-to-end privacy for legitimate users. Initially, we explore suitable transaction features within private blockchains for training machine learning classifiers to detect anomalous behaviors. Subsequently, we customize a privacy-centric classifier employing homomorphic encryption to achieve private computation of anomaly detection without leaking sensitive information from private transaction content. We then construct the zero-knowledge proof for validating the encrypted computation process. Our work pioneers in fully integrating homomorphic encryption with zero-knowledge proof, enabling credible and trustworthy verification of the homomorphic ciphertext computations. Finally, we conduct comprehensive security analysis and experimental simulations. The experimental results demonstrate the efficiency and scalability of our approach.

Zounkaraneni Ngoupayou Limbepe, Keke Gai, Jing Yu

Federated learning (FL) has emerged as an efficient machine learning (ML) method with crucial privacy protection features. It is adapted for training models in Internet of Things (IoT)-related domains, including smart healthcare systems (SHSs), where the introduction of IoT devices and technologies can arise various security and privacy concerns. However, as FL cannot solely address all privacy challenges, privacy-enhancing technologies (PETs) and blockchain are often integrated to enhance privacy protection in FL frameworks within SHSs. The critical questions remain regarding how these technologies are integrated with FL and how they contribute to enhancing privacy protection in SHSs. This survey addresses these questions by investigating the recent advancements on the combination of FL with PETs and blockchain for privacy protection in smart healthcare. First, this survey emphasizes the critical integration of PETs into the FL context. Second, to address the challenge of integrating blockchain into FL, it examines three main technical dimensions such as blockchain-enabled model storage, blockchain-enabled aggregation, and blockchain-enabled gradient upload within FL frameworks. This survey further explores how these technologies collectively ensure the integrity and confidentiality of healthcare data, highlighting their significance in building a trustworthy SHS that safeguards sensitive patient information.

G. Prabaharan, E. Bharath, Sakthitharan Subramanian, R Kaviyaraj · 6 authors

No abstract is available for this record.

Federico Barbacovi, Enrique Larraia, Paul Germouty, Wei Zhang

No abstract is available for this record.

P. K. Sinha, Nishant Kumar, Piyush Aggarwal

No abstract is available for this record.

Khoa Tan Vo, Minh Ngo, Thu Nguyen, Thu-Thuy Ta · 7 authors

Scalability remains a key challenge for layer 1 blockchains. ZK-Rollups, leveraging zero-knowledge proofs, offer a promising layer 2 solution by improving throughput and reducing costs while preserving security. However, the performance of ZK-Rollup still poses a major barrier to practical implementation. The proving circuits in popular applications like ERC-20 transactions are highly complex, often containing a large number of constraints, which directly impacts the computation time and resources required to generate zero-knowledge proofs. This study presents an empirical study on the impact of constraint optimization in Circom on the performance of ERC-20 ZK-Rollups using Groth16. Three optimization levels (–O0, –O1, –O2) are evaluated across transaction batches ranging from 4 to 128, with further exploration up to 512 for specific optimization levels to assess scalability. Results show a trade-off: –O2 reduces constraints by up to 73.2% but increases compilation time by 213.35% at batch size 128, while –O1 offers a more balanced approach suitable for development stages. Findings confirm that proof generation time is closely tied to constraint count and complexity. Based on these insights, this study introduces ZCLS (ZK-Circuit Lifecycle Strategy), a practical framework for selecting optimization flags aligned with development stages to enhance ZK-Rollup system efficiency.

Carmen Wabartha

The end-to-end verifiable e-voting system Ordinos [26] is primarily characterized by its tally-hiding property, which ensures that only the actual election result, e. g., the winner of the election, is revealed while the full tally consisting of the aggregated votes stays hidden. Ordinos is an abstract model that guarantees tally-hiding, verifiability and vote privacy if the underlying cryptographic primitives satisfy certain requirements. It uses a multi-party-computation protocol over an additively homomorphic encryption scheme and guarantees active security with zero-knowledge proofs. Ordinos has already been instantiated for several election systems using the Paillier [35] encryption scheme, which can be broken by Shor’s algorithm [41]. The aim of this thesis is to instantiate Ordinos post-quantum secure using a variant of Regev’s LWE-based cryptosystem [39], which is adapted to realize an actively secure threshold encryption scheme over an arbitrary plaintext space. Then a noise analysis of the arithmetic and logical components used in the MPC-protocol of the Paillier instantiation is conducted, and the components are slightly adapted to restrict the noise growth. Additionally, valid zero-knowledge proofs are provided and a concrete instantiation achieving a security level of 128 bits is shown.

Carmen Wabartha

The end-to-end verifiable e-voting system Ordinos [26] is primarily characterized by its tally-hiding property, which ensures that only the actual election result, e. g., the winner of the election, is revealed while the full tally consisting of the aggregated votes stays hidden. Ordinos is an abstract model that guarantees tally-hiding, verifiability and vote privacy if the underlying cryptographic primitives satisfy certain requirements. It uses a multi-party-computation protocol over an additively homomorphic encryption scheme and guarantees active security with zero-knowledge proofs. Ordinos has already been instantiated for several election systems using the Paillier [35] encryption scheme, which can be broken by Shor’s algorithm [41]. The aim of this thesis is to instantiate Ordinos post-quantum secure using a variant of Regev’s LWE-based cryptosystem [39], which is adapted to realize an actively secure threshold encryption scheme over an arbitrary plaintext space. Then a noise analysis of the arithmetic and logical components used in the MPC-protocol of the Paillier instantiation is conducted, and the components are slightly adapted to restrict the noise growth. Additionally, valid zero-knowledge proofs are provided and a concrete instantiation achieving a security level of 128 bits is shown.

Chen-Fan Chang, Ting‐Yu Chang, Chih-Chieh Chang, Te-Chuan Chiu · 6 authors

No abstract is available for this record.

Tumu Rajasekhar Babu

Federated learning represents a transformative approach in the realm of machine learning by enabling the training of models across decentralized devices while maintaining data privacy. Traditional centralized learning methods often compromise user privacy and data security by requiring the aggregation of data on a central server. In contrast, federated learning decentralizes the training process, allowing devices to collaboratively learn a shared model without exposing their private data. This paper explores the intricacies of federated learning, emphasizing its potential to enhance privacy and efficiency in AI systems. We delve into the technical architecture of federated learning, discussing key components such as data partitioning, model aggregation, and communication protocols. Furthermore, we address the challenges associated with federated learning, including data heterogeneity, communication overhead, and model convergence. Through comprehensive analysis and case studies, we demonstrate the efficacy of federated learning in various applications, from healthcare to finance. Our findings underscore the critical role of federated learning in safeguarding data privacy while optimizing the performance of machine learning models. As the demand for privacy preserving technologies continues to grow, federated learning emerges as a pivotal solution, paving the way for more secure and efficient AI systems.