Blockchain Papers

Jae Young Jang, Sooyong Jeong, Changho Seo, Hyunil Kim

No abstract is available for this record.

Shi Wang

Traditional identity authentication algorithms that rely on centralized trust authorities and plaintext identity verification often suffer from privacy leakage, key misuse, and single-point-of-failure risks. This study proposes a lightweight, privacy-preserving authentication algorithm based on elliptic curve and zero-knowledge proofs to address these issues. The proposed scheme introduces a random challenge and an anonymous verification mechanism during the authentication process to ensure both identity privacy and authentication security. While maintaining high levels of security and verifiability, the algorithm effectively reduces computational complexity and communication overhead. Experimental results demonstrate that the proposed method significantly outperforms traditional RSA and ECDSA in terms of authentication delay, communication cost, and security robustness. This approach is practical and scalable, offering a promising solution for secure authentication in environments with limited resource.

Shi Wang

As network applications rapidly evolve toward mobile and ubiquitous scenarios, identity authentication protocols face heightened demands for privacy protection and computational efficiency while maintaining security.Traditional authentication schemes often struggle to achieve an effective balance between privacy preservation, computational complexity, and security during design, with performance bottlenecks becoming increasingly prominent in resource-constrained environments.To address these challenges, this study proposes an optimized algebraic curve identity authentication protocol incorporating zero-knowledge proofs.Building upon Elliptic Curve Cryptography (ECC) as its cryptographic foundation, the protocol leverages ECC's inherent advantages of shorter key lengths and higher computational efficiency for equivalent security levels.Simultaneously, it integrates zero-knowledge proof mechanisms to minimize the exposure of user identity information during authentication.Through systematic optimization of the key generation mechanism, zero-knowledge proof interaction flow, and identity verification logic, the proposed protocol effectively reduces computational and communication overhead while ensuring identity anonymity and authentication integrity.Experimental results demonstrate that compared to traditional ECC authentication protocols and classical zero-knowledge proof schemes, the optimized protocol exhibits significant advantages in key generation time, authentication response latency, and communication load.It effectively resists common security threats such as replay attacks and forgery attacks, making it suitable for resource-constrained network environments and privacy-sensitive applications.

Ali Ibrahim Mohamed Ibrahim El-Gamal

Zero-knowledge proofs provide cryptographic guarantees of statement validity without revealing underlying secrets. However, static proofs enable linking attacks where adversaries track the same proof across multiple uses, compromising user privacy. We introduce Time-Based Re-randomization (TBR), a novel protocol that automatically transforms zero-knowledge proofs at fixed time intervals while preserving their validity. Our construction leverages cryptographic randomization combined with deterministic time-slot generation to create temporally unlinkable proofs without user interaction. We provide formal security proofs demonstrating that TBR maintains zero-knowledge and soundness properties while preventing proof-linking attacks. Performance analysis shows TBR incurs only 8-12ms overhead compared to 450-600ms for generating fresh proofs, making it practical for privacy-preserving applications including anonymous authentication, timelimited credentials, and blockchain systems.

Agathe Beaugrand, Guilhem Castagnos, Fabien Laguillaumie

No abstract is available for this record.

Shi Wang

With the rapid development of the digital economy and the Internet of Things, identity authentication in resource-constrained environments faces challenges such as low efficiency and inadequate privacy protection. Addressing the high computational and communication overhead of traditional RSA and ECC authentication mechanisms, this study proposes an efficient identity authentication mechanism (AC-ZKP) based on algebraic curves and non-interactive zero-knowledge proofs (NIZK). This mechanism leverages algebraic curve group operations to achieve lightweight key management and employs zero-knowledge proofs to ensure information concealment and anti-forgery during identity verification. The paper conducts a systematic study across four dimensions: system modeling, algorithm design, security analysis, and performance evaluation. Experimental results demonstrate that while maintaining 128-bit security strength, the AC-ZKP mechanism reduces authentication latency by approximately 44% and communication overhead by about 40%. It also exhibits strong scalability and resistance to attacks, significantly outperforming traditional ECC schemes. These findings provide a viable solution for lightweight, high-security identity authentication in IoT, edge computing, and cross-border data exchange environments.

Siddharth Verma

Proof-of-Context (PoC) protocols aim to ensure fairness and integrity in smart contract execution by cryptographically binding on-chain transactions to verifiable off-chain contextual data. Traditional consensus mechanisms (e.g., Proof-of-Work, Proof-of-Stake) focus on ordering and validation of transactions but do not address whether the contextual conditions that should govern contract execution are satisfied. In this manuscript, we propose a novel PoC framework that leverages decentralized oracles, zero-knowledge proofs, and time-stamped Merkle commitments to provide verifiable evidence that all pre-specified preconditions and environmental parameters were met at execution time. We detail the design of the protocol, implement a prototype on an Ethereum testnet using Chainlink oracles and zk-SNARKs, and conduct a performance evaluation under varying network and workload conditions. Our results show that PoC incurs a modest overhead—on average 5% additional gas cost and 200 ms added latency per proof generation—while dramatically enhancing auditability and reducing the risk of context-based manipulation or dispute. We conclude that PoC protocols offer a practical mechanism for enforcing fairness in a wide range of decentralized applications, from DeFi loans conditioned on real-world data to NFT minting events gated by dynamic criteria. Finally, we discuss the scope, limitations, and future research directions for broader deployment.

Siyu Chen, Renhong Diao, Jiameng Xu

Abstract The aim of this study is to design and implement a system that allows centralized blockchain institutions to prove their solvency. This system ensures that institutions do not misappropriate user assets and enhances trust between users and institutions. The article introduces the Groth‐16 zero‐knowledge proof algorithm from ZK‐SNARK (zero‐knowledge succinct non‐interactive argument of knowledge). The R1CS arithmetic circuit in the Groth‐16 algorithm effectively guarantees the authenticity and tamper‐resistance of the system's raw data sources. Additionally, it combines the use of Merkle Sum Trees and Sparse Merkle trees. The former enables users to perform distributed verification of solvency proofs, while the latter effectively hides the overall number of users. Finally, users verify the balances and the private key signatures of addresses in the institution's bulletin board. Together, these components form a comprehensive and distributed solvency proof solution. This solution is a pioneering solution in the field of blockchain solvency proofs and provides a secure, efficient, and privacy‐preserving method for centralized cryptocurrency service providers or Web3 enterprise custodians. It effectively addresses the challenge of proving an institution's possession of sufficient reserves to cover user assets without compromising user privacy or disclosing the institution's scale.

Cyprian Omukhwaya Sakwa, Andrew Omala Anyembe, Fagen Li

No abstract is available for this record.

Ayman Mohamed Mostafa, Ehab R. Mohamed, Asmaa Hanafy, Faeiz Alserhani · 8 authors

Identity management (IDM) systems in cloud computing struggle to securely manage user identities and access privileges in distributed environments. However, centralized IDM solutions come with high trust costs, single points of failure, and a need for appropriate security response. This paper proposes a novel decentralized IDM framework utilizing blockchain technology and automatic provisioning (AP) techniques to improve cloud computing’s security, scalability, and operational efficiency. The framework employs Ethereum smart contracts and role‐based access control (RBAC) to ensure secure, transparent, and automated management of user identities. Key features include support for single sign‐on (SSO), multifactor authentication (MFA), and delegated proof‐of‐stake (DPoS) consensus for secure transaction validation. Our proposed scheme utilizes the Ethereum blockchain and smart contracts for managing user access, ensuring transparent and immutable record‐keeping. The scheme introduces RBAC mechanisms to ensure precise privilege allocation and dynamic updates. The scheme also supports key IDM processes, including SSO, MFA, and lifecycle management of identities. The framework incorporates DPoS consensus to enhance security for efficient transaction validation and the prevention of fraud. To address fraudulent activities, the scheme uses machine learning to detect blockchain fraud with 99.1% accuracy, demonstrating robustness and efficiency for large‐scale cloud infrastructures.

Farhana Javed, Josep Mangues‐Bafalluy, Engin Zeydan, Luis Blanco

This paper proposes a blockchain-enabled framework to enhance trust, transparency, and collaboration in Open Radio Access Network (O-RAN) infrastructures through Federated Learning (FL). Traditional O-RAN architectures and centralized machine learning approaches face challenges when integrating multi-vendor environments, primarily due to lack of trust, proprietary data concerns, and limited interoperability. Our solution transitions from implicit trust, where the reliability of contributions is assumed, to explicit trust, where reputation is verifiably established on-chain. We introduce a blockchain-based reputation mechanism that evaluates the accuracy, integrity, and quality of participants’ model updates within the FL process. Smart contracts automate critical tasks-such as participant registration, model update verification, and reputation scoring-ensuring that data inputs directly influence accountability in a tamper-proof, transparent manner. By deploying the framework on a scalable Layer 2 blockchain (Polygon) testnet and proposing the use of a blockchain oracle within this architectural framework for secure off-chain computations, this work focuses on a conceptual architectural approach by aligning with O-RAN’s architecture to propose and deploy a Decentralized Application (DApp) on the blockchain. The proposed framework emphasizes a conceptual design over performance optimization and is structured to naturally benefit from ongoing improvements in blockchain scalability, which may reduce latency and enhance operational efficiency over time. Smart contracts for crucial processes and reputation calculation are included within our proposed DApp. The implementation of this work is publicly accessiblehttps://github.com/farhanajaved/Reputation_O-RAN.

Nicolò Romandini

In today's data-driven world, vast amounts of information power Machine Learning (ML) models for a wide range of applications. However, this data flow raises significant privacy concerns, as individuals are often reluctant to share personal information, especially given increasing regulations on data protection. Federated Learning (FL) offers a solution by training ML models directly on users' devices and sending only model updates to a central server. This distributed approach enables collaboration without sharing personal data, but challenges remain. Centralization may lead to server bottlenecks, reduced resilience, and fairness concerns if updates from certain devices are prioritized. Additionally, the lack of transparency and accountability can erode trust, while security risks, such as data poisoning and model inversion attacks, further complicate FL. Deployment can be costly and time-consuming, and participants may also lack incentives. Regulatory compliance, such as ensuring the right to be forgotten, adds complexity, as removing data from FL models without full retraining is challenging. This dissertation proposes integrating Distributed Ledger Technologies (DLTs) with FL to address these challenges. DLT decentralizes the aggregation process, enhancing security, transparency, and fairness through immutable record-keeping and traceability. Two DLT-based architectures are presented: one blockchain-based and the other using a Directed Acyclic Graph (DAG) for scalability. These approaches utilize Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to track contributions and verify participants. Furthermore, a DLT-based FL as a Service (FLaaS) is introduced to simplify deployment, incorporating model validation to mitigate poisoning attacks and token-based incentives to encourage participation. Additionally, this dissertation outlines design guidelines for Federated Unlearning (FU), covering key evaluation metrics, existing techniques, and future research. Finally, a new unlearning algorithm is proposed to address adversarial settings and protect model integrity. These contributions pave the way for more secure, transparent, and resilient FL systems that can meet the needs of next-generation data-driven applications.

Juraj Mariani, Ivan Homoliak

Consensus protocols used today in blockchains often rely on computational power or financial stakes - scarce resources. We propose a novel protocol using social capital - trust and influence from social interactions - as a non-transferable staking mechanism to ensure fairness and decentralization. The methodology integrates zero-knowledge proofs, verifiable credentials, a Whisk-like leader election, and an incentive scheme to prevent Sybil attacks and encourage engagement. The theoretical framework would enhance privacy and equity, though unresolved issues like off-chain bribery require further research. This work offers a new model aligned with modern social media behavior and lifestyle, with applications in finance, providing a practical insight for decentralized system development.

Foteini Baldimtsi, Konstantinos Kryptos Chalkias, François Garillot, Jonas Lindstrøm · 10 authors

No abstract is available for this record.

Abeer S. Al-Humaimeedy

No abstract is available for this record.

Wulf A. Kaal

No abstract is available for this record.

Orestis Melkonian, Wouter Swierstra, James Chapman, Sub Software Technology · 6 authors

Distributed ledgers nowadays manage substantial monetary funds in the form of cryptocurrencies such as Bitcoin, Ethereum, and Cardano. For such ledgers to be safe, operations that add new entries must be cryptographically sound - but it is less clear how to reason effectively about such ever-growing linear data structures. This paper demonstrates how distributed ledgers may be viewed as computer programs, that, when executed, transfer funds between various parties. As a result, familiar program logics, such as Hoare logic, are applied in a novel setting. Borrowing ideas from concurrent separation logic, this enables modular reasoning principles over arbitrary fragments of any ledger. All of our results have been mechanised in the Agda proof assistant.

Eshan Sud, Shirish Agarwal, Lav Upadhyay

Zero-Knowledge Proofs (ZKPs) are public key cryptosystem that enables to demonstrate that a statement which is known by them is correct without revealing the same to the verifier. ZKPs have moved in modern cryptographic systems, blockchain applications, decentralized finance (DeFi) and identity authentication systems. This paper explores the evolution of ZKPs and their significance as in secure and privacy preserving. We classify ZKPs into two groups namely interactive and non-interactive, discussing prominent protocols such as zk-SNARKs, zk-STARKs, Bulletproofs, PLONK, and Halo2. Each approach has advantages as efficiency, proof size, and computational overhead. The study further examines the multitude of applications of ZKPs, as privacy-enhanced blockchain transactions, zero-knowledge rollups for scalability, decentralized identity management, secure voting mechanisms, and regulatorycompliant financial systems. With advantages, possible limitations in scalability, lack of standardization, and vulnerabilities to emerging quantum computing threats. Due to the restrictions, hardware acceleration through GPUs and others, presents promising solutions, while new protocols such as PLONK and Halo2 seek to optimize performance to earlier developed solutions. Finally, we discuss the future trajectory of ZKPs. This review aims to provide an understanding of the current state of ZKP research, its applications, and the key challenges that need to be addressed to facilitate broader adoption.

Baseer Fatima, K. P. Kaliyamurthie

The utilization of Zero-Knowledge Proofs (ZKPs) in blockchain technology enhances privacy while simultaneously preserving transparency. Given that blockchain networks frequently elicit privacy concerns owing to the inherently public nature of transaction data, ZKPs present a viable solution by enabling parties to authenticate transactions without disclosing sensitive information. This study primarily concentrates on zk-SNARKs and zk-STARKs, which represent advanced iterations of ZKPs that enhance both privacy and scalability. By analyzing established blockchain protocols, such as Zcash and Ethereum, this research illustrates that ZKPs can effectively safeguard privacy while also facilitating scalability through mechanisms such as zkrollups, which consolidate multiple transactions into a single proof, thereby alleviating congestion on the blockchain. Additionally, ZKPs enhance the verification efficiency, thereby reducing the computational burden on blockchain networks and promoting expedited transactions. However, challenges such as computational overheads and regulatory hurdles persist, hindering the widespread implementation of ZKPs. Future research endeavors should focus on overcoming these challenges by developing more efficient algorithms and collaborating with regulatory authorities to establish clear guidelines for ZKP-based systems. The potential implications of ZKPs extend beyond blockchain technology, offering substantial advantages to sectors such as finance, healthcare, and identity management, in which secure and confidential transactions are paramount. In summary, although ZKPs possess the capacity to transform privacy within decentralized networks, further advancements are required to fully harness their potential and ensure their extensive adoption.

Akaki Mamageishvili, Benny Sudakov

We compare the total capital efficiency of secure restaking and Proof-of-Stake (PoS) protocols. First, we consider the sufficient condition for the restaking graph to be secure. The condition implies that it is always possible to transform such a restaking graph into separate secure PoS protocols. Next, we derive two main results: upper and lower bounds on the required extra stakes to add to the validators of the secure restaking graph to be able to transform it into secure PoS protocols. In particular, we show that the restaking savings compared to PoS protocols can be very large and can asymptotically grow as a square root of the number of validators. We also study a complementary question of aggregating secure PoS protocols into a secure restaking graph and provide matching lower and upper bounds on the PoS savings.

Christian Delgado‐von‐Eitzen, Manuel J. Fernández Iglesias, Luis Anido, Martín Llamas Nistal

This paper introduces a novel access control architecture based on a dual-blockchain model that separates access management from data storage to enhance security and scalability. The system enables users to submit access requests to a primary blockchain, where smart contracts dynamically verify permissions before retrieving data from a secondary, isolated blockchain. This design enforces fine-grained, account-level access control while preventing direct exposure of sensitive data. A proof of concept was implemented using Hyperledger YUI to interconnect Ethereum-based blockchains, demonstrating secure inter-chain communication and dynamic permission enforcement. The proposed solution addresses key limitations in existing blockchain infrastructures and offers a flexible, decentralized framework suitable for applications requiring robust data governance and regulatory compliance.

Chris Gilbert, Mercy Abiola Gilbert

As blockchain technology continues to evolve, the pursuit of privacy has become a significant challenge. Although the transparency and immutability of blockchain are essential features, they can unintentionally expose sensitive information. This paper investigates the potential of Zero-Knowledge Proofs (ZKPs) and Secure Multi-Party Computation (SMPC) as innovative solutions to address these privacy concerns. ZKPs facilitate the verification of information without disclosing the underlying data, thereby enhancing privacy in transactions and identity verification processes. Meanwhile, SMPC enables collaborative computations while preserving the confidentiality of inputs, which is vital for industries such as finance and healthcare. Despite their potential, these technologies encounter challenges related to complexity, scalability, and regulatory compliance. This study offers a thorough analysis of ZKPs and SMPC, their applications, and the ethical implications involved, providing valuable insights into their role in creating a secure and privacy-conscious blockchain ecosystem.

Lyudmila Kovalchuk, M. Yu. Kuznetsov, A. A. Shumskaya

The splitting attack is one of the most important attacks on the blockchain, first of all for Proof-of-Work and Proof-of-Stake consensus protocols. Currently, there are no explicit analytical formulas for evaluating its success probability, which causes some distrust in blockchain technologies. In this paper, for a simplified (but still not simple) model of a splitting attack, the recurrent formulas allowing the evaluation of the exact values of the probability that an attacker will be able to build a branch of a given length are obtained. The correctness of these formulas is verified through numerical examples using the Monte Carlo method by constructing estimates with a specified confidence level and relative error. Keywords: blockchain, Proof-of-Stake, splitting attack, stakeholder, timeslot, slotleader, recursive formulas, Monte Carlo method.

Ammar Ahmed, M. Haseeb Javed, Junaid Nasir Qureshi, Hamayun Khan · 5 authors

Federated Learning has emerged as a promising paradigm for collaborative machine learning while preserving data privacy. Federated Learning is a technique that enables a large number of users to jointly learn a shared machine learning model, managed by a centralized server while training data remains on user devices. In recent years, along with the blooming of Machine Learning (ML)-based applications and services, ensuring data privacy and security has become a critical obligation. ML-based service providers are not only confronted with difficulties in collecting and managing data across heterogeneous sources but also challenges of complying with rigorous data protection regulations such as the General Data Protection Regulation (GDPR) Federated Learning is very important to reduce data privacy risks. Federated Learning is a scheme in which several consumers work collectively to unravel machine learning problems, with a dominant collector synchronizing the procedure. This paper reviews recent advancements in privacy-preserving techniques for federated learning from a machine-learning perspective. This paper investigates the potential of Federated Learning for privacy-preserving machine learning in domains like healthcare, finance and IOT, where data privacy is paramount. We explore existing techniques to enhance privacy, including differential privacy, secure aggregation, homomorphic encryption, federated learning with encrypted, meta-learning, machine learning, privacy-preserving techniques, blockchain technology, decentralized learning, federated averaging, data privacy, searchable encryption and zero-knowledge proofs. This paper concludes with future research directions to address ongoing challenges & further enhance the effectiveness & scalability of privacy-preserving federated learning.