Bitcoin is one of the best-known cryptocurrencies, which captivated researchers with its innovative blockchain structure. Examinations of this public blockchain resulted in many proposals for improvement in terms of anonymity and privacy. Generally used methods for improvement include mixing protocols, ring signatures, zero-knowledge proofs, homomorphic commitments, and off-chain storage systems. To the best of our knowledge, in the literature, there is no study examining Bitcoin in terms of differential privacy, which is a privacy notion coming up with some mechanisms that enable running useful statistical queries without identifying any personal information. In this paper, we provide a theoretical examination of differential privacy in Bitcoin. Our motivation arises from the idea that the Bitcoin public blockchain structure can benefit from differential privacy mechanisms for improved privacy, both making anonymization and privacy breaches by direct queries impossible, and preserving the checkability of the integrity of the blockchain. We first examine the current Bitcoin implementation for four query functions using the differential privacy formulation. Then, we present the feasibility of the utilization of two differential privacy mechanisms in Bitcoin; the noise addition to the transaction amounts and the user graph perturbation. We show that these mechanisms decrease the fraction of the cases violating differential privacy, therefore they can be used for improving anonymity and privacy in Bitcoin. Moreover, we showcase the noise addition to transaction amounts by using IBM Differential Privacy Library. We compare four differential privacy mechanisms for varying privacy parameter values and determine the feasible mechanisms and the parameters.
Md. Rayhan Ahmed, A.K.M. Muzahidul Islam, Swakkhar Shatabda, Salekul Islam
Identity Management System (IDMS) refers to how users or individuals are identified and authorized to use organizational systems and services. Since traditional identity management and authentication systems rely heavily on a trusted central authority, they cannot mitigate the effects of single points of failure. As a decentralized and distributed public ledger in a peer-to- peer (P2P) network, Blockchain (BC) technology has garnered a considerable amount of attention in the field of IDMS in recent years. Through Self-Sovereign Identity (SSI), users can have full authority over their digital identity. Successful implementation of a BC-based IDMS can significantly increase the degree of privacy and security of a user’s SSI. However, the integration of BC-based IMDS to provide a user with SSI is still an unorganized area of research in its early stages of development. This article presents an extensive literature review of state-of-the-art academic publications as well as commercial market offerings regarding the applicability of BC-based SSI solutions. It also provides a detailed preliminary regarding the building blocks of blockchain technology and a progressive roadmap of IDMS solutions. In order to develop an effective BC-based IDMS solution that focuses on securing a user’s SSI, this article outline five essential components of a BC-based IDMS: authentication, integrity, privacy, trust, and simplicity. Furthermore, we perform a security analysis that outlines several types of adversarial threats that can cause potential damage to the BC-based IDMS. We identify and discuss associated issues and challenges by analyzing several notable BC-based IDMS solutions in academic literature. We also highlight potential research gaps and provide future research scope.
As the basis of cloud computing, distributed storage technology mainly studies how data centers store, organize and manage data. Blockchain has become the most secure solution for cloud storage due to its decentralization and immutability. Consensus mechanism is one of the core technologies of blockchain, which affects the transaction processing capability, security and scalability of blockchain. The current mainstream consensus algorithms such as Proof of Work, Proof of Stake, and Delegated Proof of Stake all have the problem of wasting resources. And with the explosive growth of data, cloud storage nodes store a large amount of redundant data, which inevitably increases storage overhead and computing cost. To this end, we propose to use the Proof of Retrievability with deduplication algorithm as the consensus mechanism of the blockchain system and design a blockchain consensus protocol suitable for distributed storage. First, the data integrity verification protocol in the scheme guarantees that storage nodes correctly store the data they promise to store. Second, the deduplication algorithm in the protocol can optimize data auditing, greatly reduce the need for data storage space, and improve the scalability of data transmission. In addition, the scheme uses ring signatures in the audit process to ensure user anonymity and data unlinkability, while providing highly reliable data storage, and ensuring data storage security through blockchain. Finally, we demonstrate the security of the proposed scheme and evaluate its performance. The evaluation results show that our scheme is efficient and scalable.
In recent years, Blockchain is gaining prominence as a hot topic in academic research. However, the consensus mechanism of blockchain has been criticized in terms of energy consumption and performance. Although Proof-of-Authority (PoA) consensus mechanism, as a lightweight consensus mechanism, is more efficient than traditional Proof-of-Work (PoW) and Proof-of-Stake (PoS), it suffers from the problem of centralization. To this end, on account of analyzing the shortcomings of existing consensus mechanisms, this paper proposes a dynamic reputation-based consensus mechanism for blockchain. This scheme allows nodes with reputation value higher than a threshold apply to become a monitoring node, which can monitor the behavior of validators in case that validators with excessive power cause harm to the blockchain network. At the same time, the reputation evaluation algorithm is also introduced to select nodes with high reputation to become validators in the network, thus increasing the cost of malicious behavior. In each consensus cycle, validators and monitoring nodes are dynamically updated according to the reputation value. Through security analysis, it is demonstrated that the scheme can resist the attacks of malicious nodes in the blockchain network. By simulation experiments and analysis of the scheme, the result verifies that the mechanism can effectively improve the fault tolerance of the consensus mechanism, reduce the time of consensus to guarantee the security of the system.
Electronic health records (EHRs) are digitally saved health records that provide information about a person’s health. EHRs are generally shared among healthcare stakeholders, and thus are susceptible to power failures, data misuse, a lack of privacy, security, and an audit trail, among other problems. Blockchain, on the other hand, is a groundbreaking technology that provides a distributed and decentralized environment in which nodes in a list of networks can connect to each other without the need for a central authority. It has the potential to overcome the limits of EHR management and create a more secure, decentralized, and safer environment for exchanging EHR data. Further, blockchain is a distributed ledger on which data can be stored and shared in a cryptographically secure, validated, and mutually agreed-upon manner across all mining nodes. The blockchain stores data with a high level of integrity and robustness, and it cannot be altered. When smart contracts are used to make decisions and conduct analytics with machine-learning algorithms, the results may be trusted and unquestioned. However, Blockchain is not always indestructible and suffers from scalability and complexity issues that might render it inefficient. Combining AI and blockchain technology can handled some of the drawbacks of these two technical ecosystems effectively. AI algorithms rely on data or information to learn, analyze, and reach conclusions. The performance of AI algorithms is enhanced through the data obtained from a data repository or a reliable, secure, trustworthy, and credible platform. Researchers have identified three categories of blockchain-based potential solutions for the management of electronic health records: conceptual, prototype, and implemented. The purpose of this research work is to conduct a Systematic Literature Review (SLR) to identify and assess research articles that were either conceptual or implemented to manage EHRs using blockchain technology. The study conducts a comprehensive evaluation of the literature on blockchain technology and enhanced health record management systems utilizing artificial intelligence technologies. The study examined 189 research papers collected from various publication categories. The in-depth analysis focuses on the privacy, security, accessibility, and scalability of publications. The SLR has illustrated that blockchain technology has the potential to deliver decentralization, security, and privacy that are frequently lacking in traditional EHRs. Additionally, the outcomes of the extensive analysis inform future researchers about the type of blockchain to use in their research. Additionally, methods used in healthcare are summarized per application area while their pros and cons are highlighted. Finally, the emphasized taxonomy combines blockchain and artificial intelligence, which enables us to analyze possible blockchain and artificial intelligence applications in health records management systems. The article ends with a discussion on open issues for research and future directions.
Open access
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Ammar Battah, Mohammad Madine, Ibrar Yaqoob, Khaled Salah · 6 authors
The demand for high-quality Artificial Intelligence (AI) models is ever-increasing in this digital era. However, most of the existing methods leveraged for managing the ownership, trading, and access of AI models fall short of providing traceability, transparency, audit, security, and trustful features. In this paper, we propose a solution based on blockchain and Non-fungible Tokens (NFTs) to manage ownership rights and exchange of AI models in a transparent, traceable, auditable, secure, and trustworthy manner. Smart contracts are employed to enforce ownership, ease of access, and exchange policies for the unique NFT linked to an AI model. We use decentralized storage of the InterPlanetary File System (IPFS) and proxy re-encryption oracles to securely fetch, store, and share data related to AI models. We present algorithms along with their implementation, testing, and validation details. The proposed solution is evaluated using cost and security analyses to show its affordability and resiliency against security threats and attacks. All smart contract codes are made publicly available on GitHub.
The decentralization feature of public and private blockchain-based applications is achieved by selecting different nodes as validator or Certificate Authority (CA) for each transaction. Public blockchain uses Proof of Work (PoW) to search for the validator. PoW causes an enormous amount of energy. Therefore, Proof of Stake (PoS), and Proof of Authority (PoA) emerged as alternate solutions. Selection of a newCAusingPoSorPoAalgorithms for each transaction may improve transaction security. However, a network may have a large number of transactions, and selecting aCAfor each transaction usingPoSorPoAmay cause a significant amount of block propagation delay. Moreover, an increase in the number of participant nodes may increase the block propagation delay even further. Therefore, higher block propagation delay reduces network efficiency drastically. This paper proposes a different approach to increase the efficiency of Blockchain-based Public Key Infrastructure (BC – PKI). The proposed approach creates clusters of participant nodes based on their validation time, response time, and trust. This method selects a cluster based on the budget of response time and validation time given by the node that intends to start a transaction. Thereafter, the node which has the highest trust in that cluster is chosen as aCAfor the next transaction. Instead of searching on all participant nodes, our approach searches on the nodes of the chosen cluster which reduces the searching space of theCAselection process. This research work adopts a trust evaluation approach where the trust factor is quantified based on its experience and reputation. The node trust is reevaluated after every successful and unsuccessful transaction. A node that performs more successful transactions has more trust value. The node that has a higher trust value has a higher probability to be selected as aCAfor a transaction. The trust reevaluation process is followed by the clustering process. The result shows the proposed approach can reduce ~38.5% response time and ~2.2% validation time as compared to infrastructure which does not implement clustering. Additionally, the proposedCTB – PKIcan be used in Blockchain 2.0 and Blockchain 3.0-related applications.
By supporting decentralized applications (DApps), modern blockchains have become the technology of choice for the Web3, a decentralized way for people to interact with each other. As the popularity of DApps is growing, the challenge is now to allocate shard or subnetwork resources to face the associated demand of individual DApps. Unfortunately, most sharding proposals are inherently static as they cannot be adjusted at runtime. Given that blockchains are expected to run for years without interruption, these proposals are insufficient to cope with the upcoming demand. In this paper, we present dynamic blockchain sharding, a new way to create and close shards on-demand, and adjust their size at runtime without requiring to hard fork (i.e., creating duplicated instances of the same blockchain). The novel idea is to reconfigure sharding through dedicated smart contract invocations: not only does it strengthen the security of the sharding reconfiguration, it also makes it inherently transparent as any other blockchain data. Similarly to classic sharding, our protocol relies on randomness to cope with shard-takeover attacks and on rotating nodes to cope with the bribery of a slowly-adaptive adversary. By contrast, however, our protocol is ideally suited for open networks as it does not require fully synchronous communications. To demonstrate its efficiency, we deploy it in 10 countries over 5 continents and demonstrate that its performance increases quasi-linearly with the number of shards as it reaches close to 14,000 TPS on only 8 shards.
With the development of the times, the existing social security system can no longer meet people’s needs in terms of providing transparent, distributed sharing, tamper-proof, traceable, consensus trust and trustworthy services. Furthermore, they are centralized and subject to a single point of control and failure. In this context, we propose a consortium blockchain-based solution to establish and improve social security informatization to meet the above challenges. In this article, we present specific business situations for three important social security services. Namely, apply for social insurance, apply for social assistance and social benefits online. Our proposed solution also provides a multi-party trust and data sharing mechanism, and also demonstrates the integration of blockchain and Interplanetary File System (IPFS) storage systems to facilitate the security of approval documents, photos and videos related to the processing of social security services Accessibility and traceability. It also introduces the implementation and testing details of the algorithm in the smart contract, and expounds how to apply it in the automatic approval of social security business to reduce the workload of existing manual review. Finally, by comparing with the existing system, it is discussed that our solution has great advantages in promoting the online processing of social security business, and the safe access and traceability of approval documents, photos and videos.
Zachary Stucke, Theodoros Constantinides, John Cartlidge
Transactions sent to a public blockchain network, such as Ethereum, are initially held in the mempool before they are accepted in a block. While waiting in the mempool, these âin-flightâ transactions are publicly visible and vulnerable to front-running attacks, such that malicious parties use information in the transaction for their own gain and at a direct cost to the transaction owner. In this work, we introduce open-source simulation software for identifying and mitigating these attacks on Ethereum blockchains. Designed for education and research, the software introduces simple smart contracts that elaborate front-running vulnerabilities such as displacement attacks, sandwich attacks, and priority gas auctions. Users can run these attacks in a safe environment, monitor the detailed mechanics of attacks, and mitigate attacks using the MEV-geth protocol for in-flight transaction privacy.
Abstract With an increasing affinity towards patient‐centric care, sharing real‐time sensitive data for collaboration between multiple parties with finer access control becomes critical. Most existing studies based on the blockchain technology in the medical field discuss various application scenarios and security aspects, without focusing on data ownership, secure data sharing, or finer access control. In this work, a non‐fungible token (NFT)‐based system is proposed to implement a health record marketplace. The system leverages the NFT technology to provide dual ownership along with finer access control and efficiency in data sharing. The advantage of permissioned blockchain along with InterPlanetary File System (IPFS) are taken for off‐chain data storage to improve security and efficiency. Because price determination is critical in the market, Stackelberg game theory is utilized to determine pricing strategies for both data owners and consumers. Also, to efficiently achieve finer access control, a popularity‐based adaptive NFT management scheme using reinforcement learning is proposed. Simulation experiments are carried out to demonstrate accuracy and efficiency of our proposed schemes.
It is crucial to ensure the privacy and authenticity of patients’ medical data in the medical insurance claim process, but in the current medical insurance claim process, there are some problems such as low efficiency, complex service, unreliable data and data leakage. Therefore, considering the privacy and sensitivity of patients’ medical data, we can improve the current issues by employing blockchain, smart contracts and zero-knowledge proof technology. In this paper, we propose a novel medical insurance claim scheme based on smart contracts, blockchain and zero-knowledge proof. Our scheme mainly involves two scenarios: medical insurance purchasing and medical insurance claiming. In the privacy-preserving transaction phases of the two scenarios, we can ensure the legitimacy and privacy of the transactions between the patients and the insurance companies by using a non-interactive zero-knowledge proof and the homomorphic encryption algorithm under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. In the identity privacy-preserving phases of the two scenarios, we can ensure the legitimacy and the privacy of patients’ identities by integrating Schnorr protocol and Fiat–Shamir heuristic method. The security analysis, the computation cost and the communication cost of our scheme are given. Compared with our referred schemes, the performance evaluation shows that our scheme not only meets the requirements of the legality of the medical insurance claim, but also ensures the authenticity and privacy of the patients’ medical data. Moreover, the experimental results demonstrate that our scheme is feasible and has an acceptable time overhead.
Open access
2 source records
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
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.
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.
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.
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.
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.
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.
“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.
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.
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.