Blockchain Papers

Sangita Roy, R. K. Shyamasundar

No abstract is available for this record.

Yepeng Ding, Hiroyuki Satō, Maro G. Machizawa

Data aggregation has been widely implemented as an infrastructure of data-driven systems. However, a centralized data aggregation model requires a set of strong trust assumptions to ensure security and privacy. In recent years, decentralized data aggregation has become realizable based on distributed ledger technology. Nevertheless, the lack of appropriate centralized mechanisms like identity management mechanisms carries risks such as impersonation and unauthorized access. In this paper, we propose a novel decentralized data aggregation framework by leveraging self-sovereign identity, an emerging identity model, to lift the trust assumptions in centralized models and eliminate identity-related risks. Our framework formulates the aggregation protocol regarding data persistence and acquisition aspects, considering security, efficiency, flexibility, and compatibility. Furthermore, we demonstrate the applicability of our framework via a use case study where we concretize and apply our framework in a decentralized neuroscience data aggregation scenario.

Meryam Essaid, Jungyeon Kim, Hongtaek Ju

This research presents a novel method to assess inter-chain message relay time in the Cosmos blockchain network, which employs the Inter-Blockchain Communication (IBC) protocol for blockchain interoperability. While inter-chain transactions in Cosmos lag behind intra-chain transactions, this research conducts a thorough performance evaluation, emphasizing message relay time across diverse Cosmos chains. The results show a strong association between inter-chain transaction frequency and overall transaction processing speed (TPS), underscoring the inherent trade-off between scalability and compatibility in inter-chain communication protocols. A thorough understanding of inter-chain transaction performance is essential for advancing interoperability and improving cross-blockchain network designs. As a result, this research improves our understanding of Cosmos’ functionality and provides insightful recommendations for boosting the effectiveness and scalability of inter-chain communication. The study also sheds light on message delivery patterns in the Cosmos ecosystem, showing that IBC message relay time has an average duration of 55.448 s and is distributed lognormally. Notably, the time between the commitment of the IBC RecvPacket transaction and the commitment of the IBC Acknowledgement transaction (R to A time) considerably impacts the effectiveness of message transmission, which causes delays in the IBC message relay process.

Hamed Taherdoost

Our brain is made of thousands of neurons that create a chain of different episodes stored in the human brain in a decentralized manner that shapes human conscious experience just like blockchain technology. Although the human brain can voluntarily delete some episodes stored by the mechanism of distributed neurons, this ability is limited. Thus, each episode is open to access and saved securely so that we do not confuse our childhood memories with movie characters same as blocks in a decentralized ledger (Allegri, 2021). In this paper, it is investigated that some properties of the human brain can be to some extent analogical to blockchain and that certain blockchain concepts can be applied to the development of neuroscience as knowledge of the workings of the human brain and memories. At the same time, the latest advancements in blockchain technology can be fruitful in the development of neuroscience. To closely examine the correlation between neuroscience and blockchain, decentralized ledger technology concepts and neuroscience basics are presented [1].

Jigna J. Hathaliya, Hetav Modi, Rajesh Gupta, Sudeep Tanwar

Essential tremor (ET) and Parkinson’s tremor (PST) are neurological movement disorders in which ET emerges with body part activation, while PST is recorded in the relaxed position of the patient. The medical symptoms of ET and PST are equivalent, including gait, anxiety, and muscular stiffness. In both disorders, doctors diagnose patients related to clinical evaluations during such hospital visits, leading to misdiagnosis. Machine Learning (ML) is being used to classify the ET and PST using human-based feature extraction to address this issue. Motivated by this, we applied Deep Learning (DL) to overcome the ML issue via automating feature extraction through the model itself. In this paper, we have used the integration of Gated recurrent unit (GRU) and Long short term memory (LSTM) algorithms to predict tremor severity. Initially, accelerometer sensors are used to record tremors in all three axial dimensions for each subject. Further, this data is pre-processed using the standard scalar function and scaled in-unit variance. Furthermore, this data first passed through the GRU model, and later it fed into the LSTM model to improve the model’s performance. Moreover, we employed the blockchain (BC) network to validate the performance of the trained model. we have used a smart contract to validate the identity of the researcher. The proposed model outperforms with 80.4% training accuracy and 74.1% testing accuracy. The total communication and computation cost of the proposed scheme is 448 bits and 0.056 ms. The integration of BC and DL makes a system more reliable, transparent, and accurate.

Abdullah Ayub Khan, Asif Ali Laghari, Aftab Ahmed Shaikh, Mazhar Ali Dootio · 6 authors

A brain-computer interface (BCI) affords real-time communication, significantly improving the quality of lifecycle, brain-to-internet (B2I) connectivity, and communication between the brain and external digital devices. This assistive technology innovates information and communication development paradigms, such as directly connecting the brain and multimedia devices to the cyber world. The system converts brain information to understandable signals for multimedia devices without physical interference and replaces human-based languages with the external environment control protocols. This advancement challenges and limits security severely. For this reason, the rate of attacks, malware, ransomware, and other types of vulnerabilities is increasing drastically. Another reason is the need to improve traditional procedures to investigate cyberenvironment security aspects. Also, these malicious attackers' prime objective is to harm personal information, enable content security and privacy protocols and physical systems integrity, and create high risk between system and consumers. However, security's capital importance stems from the growing number of wearables (on-body) and in-body wireless devices. These limitations affect personal and healthcare wireless networks during the communication (such as on-chain and off-chain) between human and wearable sensors (sense and transmit) and actuators. This paper presents a novel, secure Blockchain Security Module (BSM) for BCI with Multimedia Life Cycle Framework (MLCF) (BSM-BCIMLCF) that safely connects wearables while investigating the present-day BCI life cycle (BCILC) protection. It homogenizes a Blockchain-based distributed permission network approach to overcome existing challenges. The Blockchain enables assistant cybersecurity for BCI distributed applications to identify brain operations in real-time.

Mayank Raikwar, Danilo Gligoroski

Proof of Stake (PoS) based consensus provides a better mechanism than Proof of Work (PoW) consensus for extending the blockchain without significant energy waste. Most of the PoS consensus protocols derive or use some randomness to elect a leader candidate. This makes the consensus weaker and attracts more attackers to mount different attacks, e.g., long-range attacks and block withholding attacks. In PoS consensus, having more stakes gives more chances to be a leader among participating stakeholders. Therefore, most PoS protocols do not provide better fairness for the stakeholders participating in the consensus protocol. Moreover, these protocols suffer from high communication complexity for selecting a leader candidate in each consensus round. In this work, we propose a novel consensus protocol “R3V” that selects a set of leader candidates in a round-robin manner according to age. Finally, these leader candidates compete to be the block leader by solving a Verifiable Delay Function (VDF) based puzzle. We propose different methods to generate verifiable identities for the stakeholders. The identities are enrolled in the blockchain, which provides the age norm needed for the consensus. Compared with the other PoS consensus protocols, our protocol shows better resilience against most of the common attacks on PoS protocols. Additionally, it proclaims low energy consumption, less communication complexity, and better fairness.

Timothy Tzen Vun Yap, Ting Fong Ho, Hu Ng, Vik Tor Goh

<ns3:p> <ns3:bold>Background:</ns3:bold> This research uses exploratory graph analysis to analyze the transaction data of the Ethereum network. This is achieved through network visualization and mathematical and statistical modelling of the network data. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> The dataset used in this study was extracted from the Ethereum in the BigQuery public dataset, specifically selected transactions in July 2019. The transactions were firstly modelled as network graphs and then visualized using the Kamada-Kawai and force-directed graphs layouts. Further modelling was explored with classical random graph and network block, with emphasis on network cohesion, hierarchical clustering and community membership. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> Looking at the network visualization and hierarchical clustering of the data, the network shows 170 clusters, the largest having 135 members. Through random graph modelling the optimum number of clusters is shown to be 95. Referring to the generated dendrograms, notable large transactions center around the DRINK token, the Maximine Exchange, the Upbit2 Exchange and the IDEX Exchange, identified through public disclosure of their Ethereum addresses. The network graphs tend to go towards the DRINK smart contract and the Maximine Exchange, indicating deposit actions, while it is the opposite for the IDEX Exchange. Further analysis also shows a different number of communities than the expected number. Falling short of the expected 170 clusters, the model is not able to capture additional mechanism that may be present at the density and social interaction distribution level of the network. On the other hand, network block modelling shows only four major clusters out of the 170 expected clusters, an indication that the model is not able to capture the network sufficiently. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> The study was able to capture and model the interconnectedness of the system with its notion of elements, in this case, the transactions on the network. </ns3:p>

Dan Lin, Jialan Chen, Jiajing Wu, Zibin Zheng

Much of the current research in Ethereum transaction records focuses on the statistical analysis and measurements of existing data; however, the evolution mechanism of Ethereum transactions is an important, yet seldom discussed issue. In this work, we first collect the transaction data of Ethereum and build network models from a microlevel view and then use a link-prediction-based framework to quantify the impact of network characteristics on Ethereum evolution. Next, we explore the graph structure properties and the driving factors of newly generated transaction relationships. Experimental results show that the local and microscopic structure of Ethereum networks is star-shaped, and the transaction frequency of addresses has a great impact on the evolution of Ethereum transaction relationships. First-layer nodes of microstructures dominate the network evolution. Moreover, the degree of addresses is an effective basis for predicting the direction of new transactions. Potential further studies on Ethereum transaction link prediction are discussed, for example, the label effect of center addresses.

Muhammad Saad, Songqing Chen, Aziz Mohaisen

The Bitcoin network synchronization is crucial for its security against partitioning attacks. From 2014 to 2018, the Bitcoin network size has increased, while the percentage of synchronized nodes has decreased due to block propagation delay, which increases with the network size. However, in the last few months, the network synchronization has deteriorated despite a constant network size. The change in the synchronization pattern suggests that the network size is not the only factor in place, necessitating a root cause analysis of network synchronization. In this paper, we perform a root cause analysis to study four factors that affect network synchronization: the unreachable nodes, the addressing protocol, the information relaying protocol, and the network churn. Our study reveals that the unreachable nodes size is 24x the reachable network size. We also found that the network addressing protocol does not distinguish between reachable and unreachable nodes, leading to inefficiencies due to attempts to connect with unreachable nodes/addresses. We note that the outcome of this behavior is a low success rate of the outgoing connections, which reduces the average outdegree. Through measurements, we found malicious nodes that exploit this opportunity to flood the network with unreachable addresses. We also discovered that Bitcoin follows a round-robin relaying mechanism that adds a small delay in block propagation. Finally, we observe a high churn in the Bitcoin network where ≈8 % nodes leave the network every day. In the last few months the churn among synchronized nodes has doubled, which is likely the most dominant factor in decreasing network synchronization. Consolidating our insights, we propose improvements in Bitcoin Core to increase network synchronization.

Hai Jin, Xiaohai Dai, Jiang Xiao, Baochun Li · 6 authors

Federated learning (FL) has been gaining popularity as a way to provide privacy-preserving data sharing for the Internet of Medical Things (IoMT). As a complementary, blockchain technology is used in recent literature to make FL secure. However, existing blockchain-based FL (BFL) solutions do not perform well when data in a BFL cluster are sparse. A direct solution is to collect as many devices as possible to establish a large BFL cluster. However, these devices may locate in geographically distant areas and be separated by great distance, which further results in high communication latency. The high latency will lead to BFL’s low system efficiency due to frequent communications in the blockchain consensus. In this article, we propose that the large cluster should be divided into multiple smaller clusters, each in its own geographical area and organized with a BFL. In this context, we propose CFL, a cross-cluster FL system facilitated by the cross-chain technique. CFL connects multiple BFL clusters, where only a few aggregated updates are transmitted over long distances across clusters, thus improving the system efficiency. The design of CFL focuses on a cross-chain consensus protocol, which guarantees the model updates to be exchanged securely across clusters. We carry out extensive experiments to evaluate CFL in comparison with BFL, and show both CFL’s feasibility and efficiency.

Bishenghui Tao, Ivan Wang‐Hei Ho, Hong‐Ning Dai

In this paper, we conduct a complex-network analysis of the Bitcoin network. In particular, we design a new sampling method namely random walk with flying-back (RWFB) to conduct effective data sampling. We then conduct a comprehensive analysis of the Bitcoin network in terms of the degree distribution, clustering coefficient, the shortest path length, the assortativity, and the rich-club coefficient. There are several important observations from the Bitcoin network, such as small- world phenomenon and non-rich-club effect. This work brings up an in-depth understanding of the current Bitcoin blockchain network and offers implications for future directions in malicious activity and fraud detection in cryptocurrency blockchain networks.

Fredy Andrés Aponte-Novoa, Luz E Gutiérrez, Magda Pineda, Inés Meriño Fuentes · 6 authors

In recent years, Blockchain has become a disruptivetechnology to protect the integrity of information, especially inopen and collaborative information systems. Its main advantageis the possibility to reach consensus on the new data blocks tobe added to the chain, even with anonymous actors. The mostcommon consensus mechanism is Proof of Work, but it has beenproven to be very inefficient in terms of energy spent by themembers of the blockchain. In the literature there are many othertechniques that pretend to become the new popular mechanism.However, the number of this techniques is growing too fast toreally be able to differentiate among all the options. In this work,a new characterization of consensus algorithm is proposed, thatcan be used to find families of mechanism using cluster-basedclassification. Using the Ward Method and Spearmans RankCorrelation analysis, new clusters of consensus mechanisms wereidentified. The results describe the behavioral patterns not seenbefore in the literature. In addition, some open problems ofcurrent consensus algorithms are discussed.

Tianhui Meng, Y. B. Zhao, Katinka Wolter, Chengzhong Xu

Analyzing blockchain protocols is a notoriously difficult task due to the underlying large scale distributed networks. To address this problem, stochastic model-based approaches are often utilized. However, the abstract models in prior work turn out not to be adoptable to consortium blockchains as the consensus of such a blockchain often consists of multiple processes. To address the lack of efficient analysis tools, we propose a queueing network-based method for analyzing consistency properties of consortium blockchain protocols in this article. Our method provides a way to evaluate the performance of the main stages in blockchain consensus. We apply our framework to the Hyperledger Fabric system and recover key properties of the blockchain network. Using our method, we analyze the security properties of the ordering mechanism and the impact of delaying endorsement messages in consortium blockchain protocols. Then an upper bound is derived of the damage an attacker could cause who is capable of delaying the honest players' messages. Based on the proposed method, we employ analytical derivations to investigate both the security and performance features, and corroborate close agreement with measurements on a wide-area network testbed running the Hyperledger Fabric blockchain. With the proposed method, designers of future blockchains can provide a more rigorous analysis of their consortium blockchain schemes.

Aditya Ahuja

Distributed ledger technology, and specifically blockchain protocols, have been leveraged to define decentralized lotteries to circumvent challenges in traditional lottery systems. Unfortunately, lottery consensus in proof-of-resource (such as proof-of-work, proof-of-stake) blockchains is provably biased towards lottery users that command a majority of the resource determining consensus in their respective cryptocurrency networks, resulting in partial-decentralization. Further, classical consensus protocols are inherently slow in achieving consensus, and thus cannot be considered for designing fast decentralized lotteries.We present a fast, fully-decentralized computational lottery TensorFlip, where the betting users and the lottery house are peers in the same cryptocurrency network, and the bet and winning per player is a consensus based on a rudimentary quantum and classical distributed computation. We employ a two-party verifiable random function construction for computing the lottery winnings. Our lottery operates in the presence of both fail-stop and Byzantine adversaries, under the synchronous network model. Although slower classical consensus based and partially-decentralized blockchain consensus based lotteries may exist, we believe we are the first to propose a quantum and classical computation based deterministic fully-decentralized lottery, with an expected constant round complexity protocol.

Sana Naz, Scott Uk-Jin Lee

The Blockchain with PoW consensus was first developed to solve the cryptocurrency double-spending and byzantine fault problem. The PoW consensus solved the problem very effectively. But due to its high computation and specialized hardware need, a lot of questions from society has been raised on the PoW consensus. To solve the limitations of PoW, a lot of other consensuses have been proposed in the literature as an alternative solution of PoW. However, the other consensus has its own benefits and drawbacks. So, to understand the consensus mechanisms our paper first describes the internal architecture of blockchain and then explain five consensuses of the blockchain network with its detailed comparative analysis of internal characteristic. We believe this comparative analysis will be helpful for those researchers who need to understand the major characteristics that a given consensus offer for their work.

Swaroopa Reddy B, G. V. V. Sharma

In this paper, we propose a consensus protocol by considering the ledger as Directed Acyclic Graph (DAG) called blockDAG instead of chain of blocks. We propose a two-step strategy for making the system robust to double-spend attacks. The first step is the graph clustering algorithm based on spectral graph theory for separating the blocks created by the non-cooperating miners (attacker) in the blockchain network followed by the second step-the ordering algorithm based on the topological ordering of the blockDAG using the references included in block header. The first step is an unsupervised learning classification of the vertices of a graph into two classes. The simulation results show that the proposed clustering Algorithm based consensus protocol counter-attack the attacker's double-spending strategy by eliminating the attacker blocks created during attacking phase from the confirmed list of the blocks. In bitcoin's longest chain rule protocol, the ledger takes the chain of blocks and it operates with the overestimation of the network's end-to-end propagation delay which results in a low transaction throughput. Bitcoin protocol guarantees the security through longest chain rule but it suffers from the limited transaction scalability. The proposed consensus protocol works better for higher block creation rates in turn improves the transaction throughput without compromising the security of the blocks from double-spending attack.

Chien-wen Shen, Li-chin Chang, Tzu-chuan Su

Purpose This study aims to provide researchers a holistic approach for comprehensive understanding of the Bitcoin-related research by discovering its trends, subjects, relations, keywords and concepts. Design/methodology/approach An integrated approach of bibliometric analysis, network analysis and concept linking analysis was proposed for exploring Bitcoin-related studies from 70 countries in the Scopus database. Findings The bibliometric analysis shows that electronic money and blockchain are the mainstream issues of Bitcoin, and the domain distribution of the literature is mainly in engineering-related fields. Through the network analysis of cocitations, co-occurrences and cowords, research clusters were discovered respectively from different perspectives. The authors also have mastered a multilevel concept linking diagram for six related major concepts. Originality/value The major contribution of this research is about providing an integrated and comprehensive approach to extract the mainstream issues that can help researchers conduct Bitcoin-relevant research. This study shows the development trend, context and clusters of Bitcoin-related studies from various perspectives networks and produces a visual concept linking diagram that will enable researchers to quickly understand the contextual relationship between Bitcoin keywords during literature analysis. In addition, the most crucial studies in the main topics are extracted to save the considerable time and labor that would be required to manually read all the literature and summarize the issues.

Sungho Lee, Minsu Kim, Jemin Lee, Ruei‐Hau Hsu · 5 authors

Recent advances in blockchain technology have led to a significant interest in developing blockchain-based applications. While data can be retained in a blockchain, the stored values can be deleted or updated. From a user viewpoint that searches for data, it is unclear whether the discovered data from the blockchain storage is relevant for real-time decision-making processes for block-chain-based applications. The data freshness issue serves as a critical factor, especially in dynamic networks handling real-time information. In general, transactions to renew data require additional processing time inside the blockchain network, which is called ledger-commitment latency. Due to this problem, some users may receive outdated data. As a result, it is important to investigate if the blockchain is suitable for providing real-time data services. In this article, we first describe block-chain-enabled (BCE) networks with Hyperledger Fabric (HLF). Then, we define age-of-information (AoI) of BCE networks and investigate influential factors on this AoI. Experiments are conducted to explore the impacts of the influential factors on data freshness in BCE networks. Lastly, we conclude by discussing future challenges.

Maya Leshkowitz, Olivia Benattasse, Oded Wertheim, Ori Rottenstreich

A dominant part in blockchain networks is reaching an agreement on block transactions and their impact on the network state. We follow a common scenario where a node is selected to propose a block and its implied state updates. The proposal is then validated by other nodes that examine the block impact on the state. Typically, all validators execute the complete block and provide an indication based on comparing the results of their execution to the updated state in the proposal. With the increase in the number of participants in blockchain networks, we suggest a time-efficient block validation through splitting it into multiple disjoint tasks. This can be challenging due to possible dependencies between the block transactions. We describe the additional information the leader has to provide to enable that. Moreover, we describe a unique proof for the block partition computed by the leader such that when validated in part by the different committees guarantees the correctness of the execution by the leader. We compare the approach to traditional solutions based on real data of the Ethereum blockchain.

Peng Li, Kang Li, Yilei Wang, Ying Zheng · 7 authors

Summary Blockchain has started to appear as a potentially reliable and underlying technology for various fields. There have been lots of surveys focusing on blockchain with respect to specific topics, such as security, architecture, applications, and so on. However, a systematic mapping study, including all related fields about blockchain, has been largely ignored. In this article, we revisit the problem of complex networks in the form of scientific collaboration networks. More specifically, we utilize the method of systematic mapping and implement them into blockchain technology. We collect 233 articles by searching Baidu scholar with the keyword “blockchain,” then construct two complex networks according to the relationship of keywords and authors, respectively. The keywords' complex network is a small‐world network while the authors' complex network is not. Furthermore, the tool of Netdraw provides a visualized graph for the complex network. Meanwhile, we find some subgroups in the network, which may highlight the future direction of blockchain.

Hao Yin, Yihang Wei, Yuwen Li, Liehuang Zhu · 6 authors

No abstract is available for this record.

Silivanxay Phetsouvanh, Anwitaman Datta, Frédérique Oggier

Summary Distinct transactions among different and unrelated users are combined together to create a single Bitcoin transaction (mixing transaction) to obfuscate the relationships among the actual participants (more specifically, the wallet addresses used for the transactions). We consider multi‐input multi‐output transactions with at least two inputs and three outputs as proxy, to analyze four characteristic periods of ∼50 days each, representing periods before the introduction of mixing, in its early days, during its growth, and after the volume of such multi‐input multi‐output transactions became more or less stabile. Structural properties and characteristics of the transaction and wallet address networks are computed and compared, through standard tools, but also via the introduction of two novel techniques that provide indicators of mixing‐like behaviors: (1) an entropy characterization to detect abnormally uniform inputs and/or outputs and (2) a connected component analysis of subgraphs formed by only multi‐input multi‐output transactions (showing cascades of such transactions). The contributions of this exploratory Bitcoin network analysis paper can thus be seen as two‐fold. At a macroscopic level, the growth and stabilization periods are shown to stand out with respect to most considered metrics, while at a microscopic level, chains of multi‐input multi‐output transactions, and transactions with outlier behavior in terms of input/output entropies are identified for further investigation.

Tsung-Ting Kuo, Jihoon Kim, Rodney A. Gabriel

OBJECTIVE: To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks. MATERIALS AND METHODS: We propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology. RESULTS: HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level. DISCUSSION: HierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns. CONCLUSION: We demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.