Blockchain Papers

Yongkai Fan, Binyuan Xu, Linlin Zhang, Jinbao Song · 6 authors

No abstract is available for this record.

Arasu Arun, Joseph Bonneau, Jeremy Clark

No abstract is available for this record.

Tao Feng, Pu Yang, Chunyan Liu, Junli Fang · 5 authors

The data generated in the Industrial Internet of Things (IIoT) has important research value. In the process of data sharing, data privacy, security, and data availability are important issues that cannot be ignored. This paper proposes a blockchain privacy protection scheme based on zero‐knowledge proof to realize the secure sharing of data among data owners, cloud service providers, and semitrusted cloud servers. First, the method of combining zero‐knowledge proof and smart contract is used to verify the availability of data between the data owner and the cloud service provider under the premise of protecting data privacy. Second, proxy reencryption technology is used to realize the secure sharing of data among authorized cloud service providers. In addition, data sharing transaction information between multiple parties and data hashes with digital signatures are stored on the blockchain to achieve public and verifiable data sharing information and data validity. Finally, the theoretical analysis of the scheme shows that the scheme meets the confidentiality requirements of security, integrity, and validity.

Vadim Lyubashevsky, Ngoc Khanh Nguyen, Maxime Plançon

No abstract is available for this record.

Pavlos Papadopoulos, Nikolaos Pitropakis, William J. Buchanan

No abstract is available for this record.

Naipeng Li, Yuchun Guo, Yishuai Chen, Jinchuan Chai

Vehicular reputation maintenance with distributed ledger is aimed at establishing trust among vehicles randomly meeting in a Vehicular Ad‐hoc Network (VANET). It is, however, challenging in VANET, as congested areas in road networks, brought by traffic tides or accidents, challenge the ledger performance. Meanwhile, the reputation update is highly dependent on transaction consensus of the distributed ledger. To solve the problem, this paper proposes deploying directed acyclic graph‐ (DAG‐) based distributed ledgers on vehicles, which use the vehicular distribution to adapt the unpredictable reputation update. Specifically, we first propose a partitioned DAG‐based distributed ledger to manage vehicular reputation in partitioned VANET. Secondly, we introduce a novel reputation evaluation method to encourage vehicles to contribute to VANET interaction and ledger consensus maintenance, which can remedy the topology churn of the ledger network due to the mobility of VANET. Finally, we design a reputation update method based on the consistency of transactions in the partition to facilitate trust establishment. Experimental results on a real‐world dataset show that the proposed ledger and reputation update method is effective and feasible in the large‐scale dynamic VANET.

Yu Guo, Chen Zhang, Cong Wang, Xiaohua Jia

Dynamic Searchable Symmetric Encryption (DSSE) is a practical cryptographic primitive that assists servers to provide search and update functionalities in the ciphertext domain. Recent work on DSSE schemes has focused on the direction of forward-privacy, requiring that newly added files cannot be linked to previously query results. However, due to the complexity of forward-privacy updates, existing schemes can only address an honest-but-curious server. It is difficult to verify updated results while preserving forward-privacy. In this paper, we explore how blockchain techniques can help us achieve a verifiable and forward-privacy DSSE scheme. Our scheme resorts to the emerging smart contract as a trusted platform to store digests for public result verification, and carefully crafts dynamic query protocols to enable encrypted search with forward-privacy. In our design, indexes are collocated with encrypted files and stored at storage-servers, which makes the blockchain light-weighted and search operations more efficient. Moreover, we propose a hybrid index design to support efficient files deletion. By using our blockchain-assisted primitive, the property collision between dynamic result verification and forward-privacy can be solved. We formally analyze the security strengths and provide the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of our blockchain-assisted DSSE scheme.

Yujia Tang, Chang Xu, Can Zhang, Yan Wu · 5 authors

Abstract Tornado Cash, the most popular non-custodial coin mixer on Ethereum, is widely used to protect the privacy of addresses. However, some inappropriate transaction behaviors in Tornado Cash mixing mechanism lead to the risk of privacy leakage. More specifically, the malicious attackers can link multiple addresses of the same users according to the transaction data. Motivated by the above problem, this paper systematically analyzes the privacy issues of Tornado Cash for the first time. In this paper, we give the macroscopic analysis of Tornado Cash based on the on-chain data and formalize two types of transaction patterns. Focus on the presented transaction patterns, we propose three heuristic clustering rules to link the users’ addresses, which reduce the size of users’ anonymity set. Finally, we perform the experiment on real Tornado Cash transaction data to describe the effectiveness of the proposed clustering rules.

Jiajun Zhou, Chenkai Hu, Jianlei Chi, Jiajing Wu · 6 authors

Blockchain technology has the characteristics of decentralization, traceability and tamper-proof, which creates a reliable decentralized trust mechanism, further accelerating the development of blockchain finance. However, the anonymization of blockchain hinders market regulation, resulting in increasing illegal activities such as money laundering, gambling and phishing fraud on blockchain financial platforms. Thus, financial security has become a top priority in the blockchain ecosystem, calling for effective market regulation. In this paper, we consider identifying Ethereum accounts from a graph classification perspective, and propose an end-to-end graph neural network framework named Ethident, to characterize the behavior patterns of accounts and further achieve account de-anonymization. Specifically, we first construct an Account Interaction Graph (AIG) using raw Ethereum data. Then we design a hierarchical graph attention encoder named HGATE as the backbone of our framework, which can effectively characterize the node-level account features and subgraph-level behavior patterns. For alleviating account label scarcity, we further introduce contrastive self-supervision mechanism as regularization to jointly train our framework. Comprehensive experiments on Ethereum datasets demonstrate that our framework achieves superior performance in account identification, yielding 1.13% ~ 4.93% relative improvement over previous state-of-the-art. Furthermore, detailed analyses illustrate the effectiveness of Ethident in identifying and understanding the behavior of known participants in Ethereum (e.g. exchanges, miners, etc.), as well as that of the lawbreakers (e.g. phishing scammers, hackers, etc.), which may aid in risk assessment and market regulation.

Jagger S. Bellagarda, Adnan M. Abu‐Mahfouz

In recent times, Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) have become two of the most discussed sectors in Information Technology, with each having made a major impact. This has generated space for further innovation to occur in the convergence of the two technologies. In this paper, we gather, analyse, and present a detailed review of the convergence of AI and DLT in a vice versa manner. We review how AI is impacts DLT by focusing on AI-based consensus algorithms, smart contract security, selfish mining, decentralized coordination, DLT fairness, non-fungible tokens, decentralized finance, decentralized exchanges, decentralized autonomous organizations, and blockchain oracles. In terms of the impact DLT has on AI, the areas covered include AI data privacy, explainable AI, smart contract-based AIs, parachains, decentralized neural networks, Internet of Things, 5G technology and data markets, and sharing. Furthermore, we identify research gaps and discuss open research challenges in developing future directions.

Yinbin Miao, Ziteng Liu, Hongwei Li, Kim‐Kwang Raymond Choo · 5 authors

Federated learning enables clients to train a machine learning model jointly without sharing their local data. However, due to the centrality of federated learning framework and the untrustworthiness of clients, traditional federated learning solutions are vulnerable to poisoning attacks from malicious clients and servers. In this paper, we aim to mitigate the impact of the central server and malicious clients by designing a Privacy-preserving Byzantine-robust Federated Learning (PBFL) scheme based on blockchain. Specifically, we use cosine similarity to judge the malicious gradients uploaded by malicious clients. Then, we adopt fully homomorphic encryption to provide secure aggregation. Finally, we use blockchain system to facilitate transparent processes and implementation of regulations. Our formal analysis proves that our scheme achieves convergence and provides privacy protection. Our extensive experiments on different datasets demonstrate that our scheme is robust and efficient. Even if the root dataset is small, our scheme can achieve the same efficiency as FedSGD.

Guruprakash Jayabalasamy, Srinivas Koppu

“Blockchain” is a buzzword that has captured attention from researchers in almost every possible domain. In a blockchain ecosystem, multiple parties across the network have to establish trust in an untrusted environment to ensure seamless nonrepudiation. Nonrepudiation is a crucial factor for secure information auditing in the blockchain. To achieve this, we require an optimal digital signature. In this work, we design a digital signature to address the nonrepudiation challenge in blockchain ecosystems. Our approach developed in the current work completed signing and verification tasks with 10% and 13% shorter processing times, respectively than the conventional digital signature scheme. The adoption of the high-performance Edwards curve aggregate signature (HECAS) in a blockchain ecosystem can yield a 10% improvement in the transaction flow, a 10% improvement in block validation and a 40% decrease in storage costs relative to the same system implemented without HECAS. Finally, we simulated various blockchain-based Internet of Things (IoT) ecosystems and experimented with our scheme. The results show that our work can produce convincing, consistent results across various sensor data types from smart IoT-based blockchain solutions.

Esraa M. Ghourab, Mohamed Azab, Noha Ezzeldin

Considering the highly dynamic nature of the vehicular environment and the delay-sensitive wireless medium, enabling secure and reliable vehicle-to-vehicle communication becomes a very challenging task. In this paper, we propose a novel system design that uses blockchain technology to establish a high-level trust-management successful trustworthy cooperative vehicular wireless communication. The proposed system is a cross-layer approach that optimizes the best-relay selection process allowing only trustworthy relays to participate in data transmission. In this work, blockchain stores real-time information about relaying, transmitting, and receiving vehicles. The information includes channel characteristics and participation quality. The information is vetted and verified by mining vehicles. The result guides the relay selection process to block untrustworthy vehicles from participation. The entire system is comprehensively modeled and mathematically analyzed. Simulations using throughput and Bit Error Rate (BER) as evaluation metrics demonstrated the effectiveness and efficacy of the presented approach in enabling reliable wireless communication in presence of maliciously behaving vehicles. On average, the overall system throughput rate increased by$3bps$, the BER was reduced by almost by$2dB$, and the percentage of false messages decreased by 90% considering high Signal to Noise Ratio (SNR).

Danxin Wang, Xianhao Chen, Lan Zhang, Yuguang Fang · 5 authors

In a post-pandemic era with personal precautions and vaccination, the emergence of COVID-19 variants with higher transmissibility and the socio-economic reopening have raised new challenges to existing human-to-human digital contact tracing systems, where privacy, efficiency, and energy-consumption issues are major concerns. In this article, we propose a novel blockchain-based human-to-infrastructure contact tracing framework for the post-pandemic era. Specifically, our approach collects and records the interaction information between persons and predeployed anchor nodes to trace the possible contacts with confirmed patients, so as to capture the indirect contacts and reduces the energy consumption of users. To address the privacy leakage and reliability issues in contact tracing, we introduce a self-sovereign identity (SSI) model-based blockchain which enables users to gain full control of their own identities and eliminate the linkage between the identity and location information in interaction records. To further preserve the privacy of confirmed patients, we introduce the private set intersection cardinality (PSI-CA) protocol to estimate the risk of infection by only counting the number of encounters between users and confirmed patients. Two self-executed smart contracts are deployed on the SSI blockchain to perform contact tracing, which guarantees the robustness of the system. The performance analysis validates the effectiveness of our approach.

Sara Salim, Benjamin Turnbull, Nour Moustafa

Social media (SM) 3.0 integrates SM platforms, such as Facebook and Twitter, with the Internet of Things (IoT), and has a great potential to change how we interact with mobile devices, online platforms, and the world around us. This integration with end users produces large-scale and heterogeneous data sources that demand machine learning (ML)-based data analytics for decision-making and to provide security against ML and data privacy attacks. The development of privacy-aware ML models within a federated learning (FL) ecosystem can empower an entire network to learn from data in a decentralized manner. In this article, we propose a differentially privacy blockchain-based explainable FL (DP-BFL) framework by harnessing the ever-evolving power of SM 3.0 networks. This framework permits any Internet empowered device to partake and contribute data to a global privacy preserved model. In this framework, participants will upload the differentially private local updates to the miners of blockchain, where the local updates will be evaluated and rewarded. The experimental results obtained from real-world datasets, namely, SM 3.0 and MNIST, demonstrated that the proposed framework could achieve high utility, enhanced privacy, and elevated efficiency. More Specifically, the experimental analysis of our proposed framework reveals the following two key properties. First, our proposed DP-BFL yields noticeable performance improvements in the applied learning models with high privacy and comparable utility levels, in terms of accuracy and f-measure metrics, to a standard FL and centralized learning approaches under the restriction of privacy preservation. Second, given a certain number of the malicious entities, DP-BFL allowed an enhanced recognition of users' preferences in the SM 3.0 dataset and precise prediction of images' class in the MNIST dataset while mitigating the impact of the malicious entities' poisoned updates. Moreover, as the proposed DP-BFL attains DP on the local model's update, it is considered the same as the standard FL-based setting, along with some kinds of privacy preservation on the uploaded model's updates.

Rodrigo Dutra Garcia, Gowri Ramachandran, Raja Jurdak, Jó Ueyama

Real-world applications in healthcare and supply chain domains produce, exchange, and share data in a multi-stakeholder environment. Data owners want to control their data and privacy in such settings. On the other hand, data consumers demand methods to understand when, how, and who produced the data. These requirements necessitate data governance frameworks that guarantee data provenance, privacy protection, and consent management. We introduce a decentralized data governance framework based on blockchain technology and proxy re-encryption to let data owners control and track their data through privacy-enhancing and consent management mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We have used Digital e-prescription as the use case since it has multiple stakeholders and sensitive data while enabling the medical fraternity to manage patients' prescription data, involving patients as data owners, doctors and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm, Ethereum, and pyUmbral PRE show that the proposed decentralized system guarantees transparency, privacy, and trust with minimal overhead.

Yue Pang, Danshi Wang, Xinyong Wang, Jin Li · 5 authors

Telecom big data generated by telecom networks have a high economic value. Thus, telecom operators actively explore telecom big data transactions methods to minimize the possibility of leaking users’ privacy. The existing solutions do not allow the data sets to leave the database, instead only allow the buyers to send data mining algorithms to the telecom operator’s platform for training. However, this centralized platform has a high risk of tampering. In addition, the currently existing solutions cannot be used to accurately and quickly trace the information of telecom big data transactions. To address these limitations, we propose a blockchain-based reliable traceability system for telecom big data transactions using smart contracts and the InterPlanetary File System. Two types of smart contracts are developed to store transaction information for tracing. Access control strategies and a reapproval prevention strategy are designed for ensuring the safe operation of the system and avoiding the problem of favoritism and fraud. We use Ethereum as a verification platform to develop and evaluate this system. The implementation of functions, such as purchasing data sets, sending algorithms, obtaining results, and tracing transactions in the smart contract and the implementation of the proposed strategies are verified. The results demonstrate that the performance of the proposed system is better than the existing solutions, and the traceability response time is improved to the order of seconds, so as to realize the safe and efficient traceability of telecom big data transactions. In addition, Ethereum and Hyperledger Fabric v0.6 were discussed to provide insights for future development.

Yajing Xu, Zhihui Lu, Keke Gai, Qiang Duan · 7 authors

Federated learning (FL) offers a promising approach to efficient machine learning with privacy protection in distributed environments, such as Internet of Things (IoT) and mobile-edge computing (MEC). The effectiveness of FL relies on a group of participant nodes that contribute their data and computing capacities to the collaborative training of a global model. Therefore, preventing malicious nodes from adversely affecting the model training while incentivizing credible nodes to contribute to the learning process plays a crucial role in enhancing FL security and performance. Seeking to contribute to the literature, we propose a blockchain-empowered secure and incentive FL (BESIFL) paradigm in this article. Specifically, BESIFL leverages blockchain to achieve a fully decentralized FL system, where effective mechanisms for malicious node detections and incentive management are fully integrated in a unified framework. The experimental results show that the proposed BESIFL is effective in improving FL performance through its protection against malicious nodes, incentive management, and selection of credible nodes.

Kaiping Xue, Xinyi Luo, Hangyu Tian, Jianan Hong · 6 authors

In mobile communication networks, when a user roams to and accesses a foreign network, the foreign operator needs to request the user’s subscription data from a centralized authentication server, which is managed by the user’s home operator. However, centralized authentication introduces single point of failure. Meanwhile, the real-time participation of the home operator and the trust relationship between the foreign operator and the home operator are difficult to guarantee. In this paper, by adopting blockchain and smart contracts, we propose a secure and efficient access control scheme of user subscription data in roaming scenarios. We further design a flexible user authentication scheme, which utilizes derivable tokens based on the proposed access control scheme. By using blockchain to store and manage user subscription data, access control can be decentralized without any trusted third party. Besides, by implementing automatic verification of access privilege through smart contracts, the limitation of the home operators’ real-time participation is eliminated. In addition, to further improve security and reduce the on-chain storage overhead, we optimize the data encryption and storage scheme utilizing threshold secret sharing. Our security and performance analysis show that the proposed user subscription data access control scheme for roaming service provides high-level security while causing acceptable time and storage overhead.

Guangjun Wu, Shupeng Wang, Zhaolong Ning, Jun Li

Recently, the rapid developments in the Internet of Medical Things (IoMT) enable smart devices to generate and transmit massive personal electronic medical records (EMRs). However, there are many sensitive attributes in an EMR, which could be accessed by external or internal unauthorized users for malicious purposes. In this article, we present a triple subject purpose-based access control (TS-PBAC) model, which is compatible with a blockchain-enabled reliable transaction network, and design an individual-centric security and privacy-preserving mechanism for access control with different purposes and roles in IoMT scenarios. Specifically, we design hierarchical purpose tree (HPT) and related policies to guarantee the legality of an external user with different purposes. To improve the privacy for sensitive attributes against an internal attacker, we design a local differential privacy (LDP)-based policy and role-based access control scheme in an edge computing paradigm to grant fine-granularity rights for authorized users. In addition, we introduce mutual evaluation metrics to evaluate data quality from a patient-and-medical-service level in an open anonymous network, only using logs kept in the blockchain. We test our approach by real-world EMRs with 100000 patients. The experimental results show that the proposed privacy-preserving scheme can better protect patient’s privacy than traditional access control policies in IoMT environments, and can make reliable and stable access control decisions between data publishers and data requesters with different purposes.

Mohamed Chahine Ghanem, Fadi Dawoud, Habiba Gamal, Eslam Soliman · 6 authors

The rapid expansion of data worldwide invites the need for more distributed solutions in order to apply machine learning on a much wider scale. The resultant distributed learning systems can have various degrees of centralization. In this work, we demonstrate our solution FLoBC for building a generic decentralized federated learning system using the blockchain technology, accommodating any machine learning model that is compatible with gradient descent optimization. We present our system design comprising the two decentralized actors: trainer and validator, alongside our methodology for ensuring reliable and efficient operation of said system. Finally, we utilize FLoBC as an experimental sandbox to compare and contrast the effects of trainer-to-validator ratio, reward-penalty policy, and model synchronization schemes on the overall system performance, ultimately showing by example that a decentralized federated learning system is indeed a feasible alternative to more centralized architectures.

Leyou Zhang, Tianshuai Zhang, Qing Wu, Yi Mu · 5 authors

Personal health records (PHRs) are located in a patient-centered electronic health system in which users can store and share medical information. However, PHRs have recently been plagued by security issues, such as the leakage of personal health information, illegal access to patient data, and data tampering. Recent security developments, such as introducing an access control policy with attribute-based encryption (ABE) or utilizing blockchain, have only been partially successful in solving these issues. Ongoing challenges to PHR sharing include single points of failure, node cheating attacks, and fair keyword search issues. In this article, we tackle these challenges by introducing a distributed PHR-sharing scheme based on blockchain and ciphertext policy ABE (CP-ABE), which allows for fast and efficient encryption and decryption. Blockchain maintains the integrity and the tracing source of the data while also recording all operations on the data in the form of transactions. In addition, the blockchain nodes act as attribute authorities to construct the CP-ABE cryptosystem. The tracing of malicious blockchain nodes is realized by tracing cryptography algorithms. Furthermore, the fair retrieval of ciphertext is achieved by employing smart contracts. To overcome the limited storage capacity of blockchain, we adopt both the on-chain and off-chain storage modes in our new system. Security analysis indicates that our new scheme remains intact when threatened by an indistinguishable chosen plaintext attack (IND-CPA) and an indistinguishable chosen keywords attack (IND-CKA). As such, we conclude that our proposed approach is feasible and efficient.

Thar Baker, Muhammad Asim, Hezekiah Samwini, Nauman Shamim · 6 authors

No abstract is available for this record.

Othmane Hireche, Chafika Benzaïd, Tarik Taleb

Along with the high demand for network connectivity from both end-users and service providers, networks have become highly complex; and so has become their lifecycle management. Recent advances in automation, data analysis, artificial intelligence, distributed ledger technologies (e.g., Blockchain), and data plane programming techniques have sparked the hope of the researchers’ community in exploring and leveraging these techniques towards realizing the much-needed vision of trustworthy self-driving networks (SelfDNs). In this vein, this article proposes a novel framework to empower fully distributed trustworthy SelfDNs across multiple domains. The framework vision is achieved by exploiting (i) the capabilities of programmable data planes to enable real-time in-network telemetry collection; (ii) the potential of P4 – as an important example of data plane programming languages – and AI to (re)write the source code of network components in a fashion that the network becomes capable of automatically translating a policy intent into executable actions that can be enforced on the network components; and (iii) the potential of blockchain and federated learning to enable decentralized, secure and trustable knowledge sharing between domains. A relevant use case is introduced and discussed to demonstrate the feasibility of the intended vision. Encouraging results are obtained and discussed.