Blockchain Papers

Daniel Shadung, Sthembile Mthethwa, Sthembile Ntshangase, Tanita Singano · 5 authors

No abstract is available for this record.

Wulf A. Kaal

No abstract is available for this record.

Khizar Hameed, Faiqa Maqsood, Zhenfei Wang

No abstract is available for this record.

Tianmin Xiong, Zhao Zhang, Cheqin Jing

No abstract is available for this record.

Mustafa TANRIVERDİ

Today, educational data, controlled centrally by educational institutions and administrative units, may be vulnerable to damage caused by natural disasters, political instability, and wars. Simultaneously, challenges arise in accessing this data for educational activities within the framework of exchange programs or lifelong learning. In the literature, there are numerous blockchain-based studies focusing on storing and sharing data in various fields. While several studies exist on blockchain applications for certification, verification, and data sharing in the education sector, a fully decentralized infrastructure has not yet been presented. To address this issue, it is proposed that data control should shift to the hands of students, who are the rightful owners of the data, rather than being solely in the hands of educational institutions. In alignment with the decentralized internet vision, Web3, public blockchain networks are considered the most suitable infrastructure for this purpose. To meet this need, a framework named PublicEduChain has been introduced within the scope of this study. PublicEduChain allows students to store their data in smart contracts created on the public Ethereum network, making it possible to share this information with any educational institution and administrative units. Educational institutions can access student data stored in smart contracts on the public Ethereum network through Learning Management System (LMS) applications and can add data to these contracts. PublicEduChain ensures that data is managed under student ownership within a fully decentralized infrastructure. The practical steps in PublicEduChain, such as creating a smart contract, logging into LMSs with Ethereum IDs, and allowing LMSs to read and write data in the student contract, are explained in detail.

Shahbaz Siddiqui, Sufian Hameed, Syed Attique Shah, Junaid Arshad · 6 authors

Smart cities represent a promising paradigm aimed at enhancing citizens’ quality of life through cutting-edge infrastructure and technological advancements. Collaborative services serve as a cornerstone for any smart city, fostering seamless cooperation among diverse entities, including government agencies, businesses, and individuals, thereby enhancing community outcomes. These services are pivotal, promoting seamless communication and collaboration among various smart applications, and facilitating data exchange, resource sharing, and functional interactions within smart city environments to optimize efficiency, effectiveness, and user experiences. However, the development and deployment of secure, interoperable services in smart cities present significant challenges. These issues encompass, ensuring data security compliance during interoperation, effective management of interconnected services, securely handling sensitive data across services, and addressing issues related to confidentiality, integrity, and availability (CIA) traits. To tackle these challenges, this research proposes an innovative adaptive security governance framework tailored for smart cities. This framework relies on dynamic security policies implemented through smart contracts to guarantee data security and privacy during smart service interoperation. Real-world use cases in collaborative smart city environments validate the framework, integrating multi-chain blockchain technology, smart services APIs, and Software-Defined Networking (SDN), showcasing its ability to enhance security and efficiency in collaborative services. This study contributes to the development of safe and efficient collaborative services inside smart cities, tackling administrative issues while emphasizing data security and privacy. Smart cities may improve citizens’ living conditions while successfully addressing crucial security problems in an ever-changing environment by using this architecture.

Ahmed Abdelmoamen Ahmed, Oluwayemisi O. Alabi

With the wide adoption of cryptocurrency, blockchain technologies have become the foundation of such digital currencies. However, this adoption has been accompanied by a surge in cryptocurrency fraud, causing significant losses to financial organizations and individuals. One way to mitigate these losses is to use Federated Learning (FL) techniques to detect fraudulent cryptocurrency transactions. This paper provides an overview of secure, privacy-preserving, and scalable Blockchain-based Federated Learning (BCFL) as a promising solution for slowing the exponential growth of cryptocurrency fraud. BCFL enables multiple entities to collaboratively train machine learning models for detecting fraudulent cryptocurrency transactions without sharing their private data, thus preserving privacy. However, Integrating differential privacy and Secure Multi-party computation (SMPC) models in BCFL presents an additional scalability challenge. This study provides an overview of BCFL, evaluating existing research on its security, privacy, and scalability challenges in detecting cryptocurrency fraud. The review explores existing research and various methodologies, highlighting advancements and challenges in creating effective, privacy-conscious fraud detection solutions for cryptocurrency transactions. We first discuss the current state of BCFL in fraud detection, along with its potential advantages and limitations, and then discuss the existing research gaps. In particular, this paper examines various BCFL frameworks, consensus algorithms, and block architectures, emphasizing their strengths and limitations in the context of cryptocurrency fraud detection to develop scalable and privacy-preserving solutions. We compare various solutions that address scalability and privacy challenges in BCFL, including adopting a geographically distributed cloud computing model that utilizes SMPC and lightweight consensus algorithms and protocols to manage computational overheads.

Bertalan Zoltán Péter, Imre Kocsis

Privacy and auditability have been conflicting design requirements for blockchainbased distributed ledgers since the inception of the field.As purpose-built blockchains with permissioned consensus and client access are developing in a broad and diverse range of industries, a specific form of this dichotomy is emerging: the need to audit the handling of regulated on-ledger financial assets, such as central bank digital currencies, while preserving the privacy and confidentiality of transactions as much as possible.This paper proposes a novel, privacy-preserving, noninteractive-zero-knowledge-proof-based protocol for a blockchain-based distributed ledger, to prove conformance with fundamental compliance requirements to external auditing parties.We present an extendable implementation and demonstrate the practicality of the approach.

Kiran K. Garimella, Daniel G. Conway

Abstract Building trust in modern business and in social interactions is a critical need as our networks continue to grow and as we engage deeply with unknown people and companies from various parts of the planet. Zero-Knowledge (ZK) technology is a powerful enabler of this trust. It allows a person to demonstrate they know something, have something, or can do something without revealing the actual information or process. We describe the fundamental concepts of cryptography and ZK, their use and limitations, and discuss how ZK technology can be used from a personal as well as a business perspective.

Ahmed Mateen Buttar, Muhammad Anwar Shahid, Muhammad Nouman Arshad, Muhammad Azeem Akbar

No abstract is available for this record.

Bruno Ramos-Cruz, Francisco J. Quesada, Mercedes Rodríguez-García, Javier Andreu-Pérez · 5 authors

No abstract is available for this record.

Jerry Huang, Ken Huang, Mudi Xu

No abstract is available for this record.

Mary C. Lacity, Erran Carmel

Abstract This chapter has two purposes. First, we describe how information system (IS) scholars approach privacy research and summarize major findings. IS scholars are concerned with information privacy and have discovered that individuals have serious information privacy concerns. These concerns, however, do not prevent individuals from disclosing personal identifiable information (PII) with centralized platform providers, a phenomenon called the privacy paradox . We highlight four common explanations for the privacy paradox: privacy calculus, privacy fatigue, trust, and lack of choice. Most IS research investigated Web2 applications. Web2 is the foundation for today’s global economy. With Web2, users rely on centralized platforms for online searching, shopping, banking, data storage, social media, and other services. Second, we introduce readers to the new paradigm of Web3. Privacy protection has been the paramount logic behind the grand design of Web3 applications. Web3 is the era of the Internet that is based on decentralized infrastructures and applications, like Bitcoin and Ethereum. Web3 applications enhance information privacy compared to Web2 because individuals can access services without disclosing PII to a central authority. The privacy objective is achieved technically through a combination of digital wallets, cryptography, and distributed ledgers (a.k.a blockchain). While Web3 is still in its early days, education is an important driver of adoption.

Y. J. Yoon, Hack-Yoon Kim, Hyunjun Jung

현대의 취업 시장에서는 허위 경력 제공이라는 문제로 인해 신뢰성 있는 경력 증명의 중요성이 부각되고 있다. 기존에 사용되던 경력 인증서는 위변조가 가능하다는 단점을 가지고 있어, 이를 해결하기 위한 대안이 필요하게 되었다. 이러한 배경 속에서 이 논문은 블록체인 기반 스마트 컨트랙트와 NFT를 활용하여 보안성과 신뢰성을 강화한 새로운 형태의 경력 인증서 발급 시스템을 제안한다. 제안하는 시스템은 경력 NFT 인증서를 발급함으로써 기존의 경력 증명 방식보다 높은 수준의 보안성을 제공한다. 이렇게 함으로써, 우리의 시스템은 신뢰할 수 있는 경력 증명 방법을 제공하며, 따라서 취업 시장에서의 신뢰도와 효율성을 향상시킬 수 있다.

Xiao-Yang Liu, Rongyi Zhu, Daochen Zha, Jiechao Gao · 7 authors

The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science. However, concerns regarding data privacy have emerged, especially when multiple stakeholders aim to collaboratively enhance LLMs using sensitive data. In this scenario, federated learning becomes a natural choice, allowing decentralized fine-tuning without exposing raw data to central servers. Motivated by this, we investigate how data privacy can be ensured in LLM fine-tuning through practical federated learning approaches, enabling secure contributions from multiple parties to enhance LLMs. Yet, challenges arise: (1) despite avoiding raw data exposure, there is a risk of inferring sensitive information from model outputs, and (2) federated learning for LLMs incurs notable communication overhead. To address these challenges, this article introduces DP-LoRA, a novel federated learning algorithm tailored for LLMs. DP-LoRA preserves data privacy by employing a Gaussian mechanism that adds noise in weight updates, maintaining individual data privacy while facilitating collaborative model training. Moreover, DP-LoRA optimizes communication efficiency via low-rank adaptation, minimizing the transmission of updated weights during distributed training. The experimental results across medical, financial, and general datasets using various LLMs demonstrate that DP-LoRA effectively ensures strict privacy constraints while minimizing communication overhead.

Qiong Li, Wennan Wang, Yizhao Zhu, Zuobin Ying

In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle models while addressing data privacy and trust challenges. We deploy resilient countermeasures, incentivized mechanisms, and a secure gradient distribution to combat single-point failure verification attacks. Additionally, we integrate the Byzantine fault-tolerant algorithm (BFT) into the node verification component of the delegated proof of stake (DPoS) to minimize verification delays. We validate our approach through experiments on the MNIST, SVHN, and CIFAR-10 datasets, showing convergence rates and prediction accuracy comparable to traditional OppCL approaches.

Qifan Mao, Liangliang Wang, Long Yu, Lidong Han · 6 authors

No abstract is available for this record.

Ghassan Al-Sumaidaee, Željko Žilić

In an era dominated by rapid digitalization of sensed data, the secure exchange of sensitive information poses a critical challenge across various sectors. Established techniques, particularly in emerging technologies like the Internet of Things (IoT), grapple with inherent risks in ensuring data confidentiality, integrity, and vulnerabilities to evolving cyber threats. Blockchain technology, known for its decentralized and tamper-resistant characteristics, stands as a reliable solution for secure data exchange. However, the persistent challenge lies in protecting sensitive information amidst evolving digital landscapes. Among the burgeoning applications of blockchain technology, non-fungible tokens (NFTs) have emerged as digital certificates of ownership, securely recording various types of data on a distributed ledger. Unlike traditional data storage methods, NFTs offer several advantages for secure information exchange. Firstly, their tamperproof nature guarantees the authenticity and integrity of the data. Secondly, NFTs can hold both immutable and mutable data within the same token, simplifying management and access control. Moving beyond their conventional association with art and collectibles, this paper presents a novel approach that utilizes NFTs as dynamic carriers for sensitive information. Our solution leverages the immutable NFT data to serve as a secure data pointer, while the mutable NFT data holds sensitive information protected by steganography. Steganography embeds the data within the NFT, making them invisible to unauthorized eyes, while facilitating portability. This dual approach ensures both data integrity and authorized access, even in the face of evolving digital threats. A performance analysis confirms the approach's effectiveness, demonstrating its reliability, robustness, and resilience against attacks on hidden data. This paves the way for secure data transmission across diverse industries.

Zongjin Li, Jie Zhang, Jian Zhang, Zheng Ya · 5 authors

Medical data sharing is crucial to enhance diagnostic efficiency and improve the quality of medical data analysis. However, related endeavors face obstacles due to insufficient collaboration among medical institutions, and traditional cloud-based sharing platforms lead to concerns regarding security and privacy. To overcome these challenges, the paper introduces MSNET, a novel framework that seamlessly combines blockchain and edge computing. Data traceability and access control are ensured by employing blockchain as a security layer. The blockchain stores only data summaries instead of complete medical data, thus enhancing scalability and transaction efficiency. The raw medical data are securely processed on edge servers within each institution, with data standardization and keyword extraction. To facilitate data access and sharing among institutions, smart contracts are designed to promote transparency and data accuracy. Moreover, a supervision mechanism is established to maintain a trusted environment, provide reliable evidence against dubious data-sharing practices, and encourage institutions to share data voluntarily. This novel framework effectively overcomes the limitations of traditional blockchain solutions, offering an efficient and secure method for medical data sharing and thereby fostering collaboration and innovation in the healthcare industry.

Yujie Hong, Liang Yang, Wei Liang, Anke Xie

The popularization of electronic health records (EHRs) effectively improves the efficiency of diagnosis and treatment of diseases, however, it also lays a hidden danger for the leakage of patients’ privacy, so it needs a stricter and more flexible access control mechanism. In addition, the medical ministry (MM) usually investigates illegal medical activities after they have already taken place and caused harm, resulting in a serious lag in regulation. To solve these problems, we propose a blockchain-based system that enables patient-leading fine-grained access control against EHRs. In contrast to the currently existing systems, this scheme uses blank EHRs as the medium, combining attribute-based encryption and blockchain to enable MM to engage in the regulation of medical activities before they taking place. In order to reduce the storage cost of the whole system, we apply the chameleon hash function to the process of calculating file storage addresses in the inter-planetary file system. Moreover, the introduction of single sign-on can improve the security and efficiency of patients’ vital signs transmission in telemedicine scenario, and the introduction of proxy re-encryption can improve the efficiency of authorization of EHRs. Theoretical analysis and experiments show that the scheme satisfies both the security and feasibility requirements.

Ghazaleh Keshavarzkalhori, Cristina Pérez‐Solà, Guillermo Navarro‐Arribas, Jordi Herrera‐Joancomartí · 5 authors

Federated learning (FL) has emerged as an alternative to traditional machine learning in scenarios where training data is sensitive. In federated learning, training is held at end devices, and thus data does not need to leave users devices. However, most approaches to federated learning rely on a central server to coordinate the learning process which, in turn, introduces its own security and privacy problems. We propose Federify, a decentralized federated learning framework based on blockchain which employs homomorphic encryption and zero knowledge proofs to provide security, privacy, and transparency. The scheme preserves the confidentiality of both the data used for training and the local models using homomorphic encryption. zkSNARKs are used to provide security by verifying the contributions from the different agents, and transparency of both the learning process and the incentive mechanism is achieved by delegating coordination into a smart contract in a public blockchain. We have also implemented, deployed, and evaluated a proof of concept of our framework, to demonstrate its viability both in terms of computational resources needed and cost to train in a public generic blockchain such as Ethereum.

Jing Liu, Xuesong Hai, Keqin Li

Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their local data directly. Data resources are difficult to aggregate effectively, resulting in a lack of support for model training. How to collaborate between data sources in order to aggregate the value of data resources is therefore an important research question. However, existing distributed-collaborative-learning architectures still face serious challenges in collaborating between nodes that lack mutual trust, with security and trust issues seriously affecting the confidence and willingness of data sources to participate in collaboration. Blockchain technology provides trusted distributed storage and computing, and combining it with collaboration between data sources to build trusted distributed-collaborative-learning architectures is an extremely valuable research direction for application. We propose a trusted distributed-collaborative-learning mechanism based on blockchain smart contracts. Firstly, the mechanism uses blockchain smart contracts to define and encapsulate collaborative behaviours, relationships and norms between distributed collaborative nodes. Secondly, we propose a model-fusion method based on feature fusion, which replaces the direct sharing of local data resources with distributed-model collaborative training and organises distributed data resources for distributed collaboration to improve model performance. Finally, in order to verify the trustworthiness and usability of the proposed mechanism, on the one hand, we implement formal modelling and verification of the smart contract by using Coloured Petri Net and prove that the mechanism satisfies the expected trustworthiness properties by verifying the formal model of the smart contract associated with the mechanism. On the other hand, the model-fusion method based on feature fusion is evaluated in different datasets and collaboration scenarios, while a typical collaborative-learning case is implemented for a comprehensive analysis and validation of the mechanism. The experimental results show that the proposed mechanism can provide a trusted and fair collaboration infrastructure for distributed-collaboration nodes that lack mutual trust and organise decentralised data resources for collaborative model training to develop effective global models.

Jinsheng Yang, Wenfeng Zhang, Zhaohui Guo, Zhen Gao

Federated learning is a privacy-preserving machine learning framework where multiple data owners collaborate to train a global model under the orchestra of a central server. The local training results from trainers should be submitted to the central server for model aggregation and update. Busy central server and malicious trainers can introduce the issues of a single point of failure and model poisoning attacks. To address the above issues, the trusty decentralized federated learning (called TrustDFL) framework has been proposed in this paper based on the zero-knowledge proof scheme, blockchain, and smart contracts, which provides enhanced security and higher efficiency for model aggregation. Specifically, Groth 16 is applied to generate the proof for the local model training, including the forward and backward propagation processes. The proofs are attached as the payloads to the transactions, which are broadcast into the blockchain network and executed by the miners. With the support of smart contracts, the contributions of the trainers could be verified automatically under the economic incentive, where the blockchain records all exchanged data as the trust anchor in multi-party scenarios. In addition, IPFS (InterPlanetary File System) is introduced to alleviate the storage and communication overhead brought by local and global models. The theoretical analysis and estimation results show that the TrustDFL efficiently avoids model poisoning attacks without leaking the local secrets, ensuring the global model’s accuracy to be trained.

Yishan Chen, B Li, Wei Li, Bowen Zeng · 6 authors

Modern IoT industry cannot operate well without task scheduling, while the merging of edge computing and blockchain can empower more secure task scheduling models in distributed IoT environments. Due to various desirable properties (self-verifying, self-executing, immutability, reliability, confidentiality, etc.) provided by blockchain, the tasks generated from IoT devices can be safely scheduled. However, merely incorporating a blockchain framework into an IoT edge computing environment cannot guarantee the most desirable rewards for task participants. So in this article, we propose to use a Nash bargaining method in blockchain smart contract designing to strive the rewards for task participants and stimulate their proactivity while extending the above properties. The proposed blockchain-based Nash bargaining architecture can be applied in many distributed IoT scenarios, such as smart logistics, health data exchange, etc. For a practical implementation, a configurable blockchain architecture is developed consisting of both regular and constrained IoT devices. We additionally implement mutual authentication protocol and proof of authority to maintain the privacy and security for scheduling. Further, we experimentally study the feasibility of such architecture built in static and mobile IoT devices. Evaluations demonstrate that, the proposed architecture can well support a task scheduling process with tamper resistance, which is suitable for IoT edge computing with high-level security and creditability.