Improvements in fabrication devices such as 3D printers are becoming possible for personal fabrication to freely fabricate any products. To clarify who is liable for the product, the fabricator should keep the fabrication history in an immutable and sustainably accessible manner. In this paper, we propose a new scheme, "Fabchain," that can record the fabrication history in such a manner. By utilizing a scheme that employs a blockchain as an audit-able communication channel, Fabchain manages print jobs for the fabricator's 3D printer over the blockchain, while maintaining a history of a print job. We implemented Fabchain on Ethereum and evaluated the performance for recording a print job. Our results demonstrate that Fabchain can complete communication of a print job sequence in less than 1 minute on the Ethereum test network. We conclude that Fabchain can manage a print job in a reasonable duration for 3D printing, while satisfying the requirements for immutability and sustainability.
While the physical lives of many of us are in democracies (one person, one vote - e.g., the EU and the US), our digital lives are mostly in autocracies (one person, all votes - e.g., Facebook). Cryptocurrencies promise liberation but stop short, at plutocracy (one coin, one vote). What would it take for us to live our digital lives in a digital democracy? This paper offers a vision, a theoretical framework, and an architecture for a grassroots network of autonomous, people-owned, people-operated, and people-governed digital communities, namely a grassroots democratic metaverse. It also charts a roadmap towards realizing it, and identifies unexplored territory for further research.
A movement for a more transparent and decentralized Internet is globally attracting more attention. People are becoming more privacy-aware of their online identities and data. The Internet is constantly evolving. Web2 focused on companies that provide services in exchange for personal user data. Web3 commits to user-centricity using decentralization and zero-server architectures. The current digital society demands a global change to empower citizens and take back control. Citizens are locked into big-tech for personal data storage and their for-profit digital identity. Protection of data has proven to be essential, especially due to increased home Internet traffic during the COVID pandemic. Citizens do not possess their own travel documents. The European Commission aims to transition this governmental property towards self-sovereign identity, introducing many new opportunities. Citizens are locked into banks with non-portable IBAN accounts and unsustainable legacy banking infrastructures. Migration to all-digital low-fraud infrastructures and healthier competitive ecosystems is essential. The overall challenge is to return the power to citizens and users again. The transition to a more decentralized Internet is the first crucial step in the realization of user-centricity. This thesis presents the first exploratory study that integrates governmental-issued travel documents into a (decentralized) societal infrastructure. These self-sovereign identities form the authentic base to a private and secure transfer of money and data, and can effectively provide trust in authenticity that is currently missing in online conversations. A fully operational zero-server infrastructure that incorporates all our requirements has been developed for Android using the P2P network overlay IPv8, and a personalized blockchain called TrustChain...
In the last few years, electric utility companies have increasingly invested into transactive energy systems. This trend was primarily caused by the integration of distributed energy resources (DERs) and internet-of-things (IoT) devices into their existing distribution networks. Influenced by the general interest in blockchain technologies, many industry specialists are considering new, more efficient peer-to-peer market structures for DERs. Since blockchain-based energy exchanges can automate transactions between their members and provide increased levels of security thanks to smart contracts, these new initiatives may eventually revolutionize how customers interact with utility companies. In this paper, we explore the trade-off between cost and traceability in the form of on-chain and off-chain solutions. We also propose ZipZap, a first step towards a blockchain-based local smart grid system. ZipZap is an ERC-1155 compliant solution with four different prototypes: Heavyweight, Featherweight, Lightweight and Weightless. The first three prototypes were developed in Solidity and deployed using Ethereum. Heavyweight is fully on-chain, whereas Featherweight and Lightweight showcase various levels of hybridization. Weightless, in turn, was deployed using Quorum, a gas-free alternative to Ethereum. Our evaluation uses realistic parameters and measures the impact of different types of metadata storage scopes, with some Ethereum prototypes showcasing gas cost reductions of more than 97% in comparison to our fully on-chain baseline.
Byzantine consensus is a critical component in many permissioned Blockchains and distributed ledgers. We propose a new paradigm for designing BFT protocols called DQBFT that addresses three major performance and scalability challenges that plague past protocols: (i) high communication costs to reach geo-distributed agreement, (ii) uneven resource utilization hampering performance, and (iii) performance degradation under varying node and network conditions and high-contention workloads. Specifically, DQBFT divides consensus into two parts: 1) durable command replication without a global order, and 2) consistent global ordering of commands across all replicas. DQBFT achieves this by decentralizing the heavy task of replicating commands while centralizing the ordering process. Under the new paradigm, we develop a new protocol, Destiny that uses a combination of three techniques to achieve high performance and scalability: using a trusted subsystem to decrease consensus's quorum size, using threshold signatures to attain linear communication costs, reducing client communication. Our evaluations on 300-replica geo-distributed deployment reveal that DQBFT protocols achieve significant performance gains over prior art: $\approx$3x better throughput and $\approx$50\% better latency.
A core enabler for blockchain or DLT interoperability is the ability to atomically exchange assets held by mutually untrusting owners on different ledgers. This atomic swap problem has been well-studied, with the Hash Time Locked Contract (HTLC) emerging as a canonical solution. HTLC ensures atomicity of exchange, albeit with caveats for node failure and timeliness of claims. But a bigger limitation of HTLC is that it only applies to a model consisting of two adversarial parties having sole ownership of a single asset in each ledger. Realistic extensions of the model in which assets may be jointly owned by multiple parties, all of whose consents are required for exchanges, or where multiple assets must be exchanged for one, are susceptible to collusion attacks and hence cannot be handled by HTLC. In this paper, we generalize the model of asset exchanges across DLT networks and present a taxonomy of use cases, describe the threat model, and propose MPHTLC, an augmented HTLC protocol for atomic multi-owner-and-asset exchanges. We analyze the correctness, safety, and application scope of MPHTLC. As proof-of-concept, we show how MPHTLC primitives can be implemented in networks built on Hyperledger Fabric and Corda, and how MPHTLC can be implemented in the Hyperledger Labs Weaver framework by augmenting its existing HTLC protocol.
Visara Urovi, Vikas Jaiman, Arno Angerer, Michel Dumontier
Easy access to data is one of the main avenues to accelerate scientific research. As a key element of scientific innovations, data sharing allows the reproduction of results and helps prevent data fabrication, falsification, and misuse. Although the research benefits from data reuse are widely acknowledged, the data collections existing today are still kept in silos. Indeed, monitoring what happens to data once they have been handed to a third party is currently not feasible within the current data sharing practices. We propose a blockchain-based system to trace data collections and potentially create a more trustworthy data sharing process. In this paper, we present the LUCE (License accoUntability and CompliancE) architecture as a decentralized blockchain-based platform supporting data sharing and reuse. LUCE is designed to provide full transparency on what happens to the data after they are shared with third parties. The contributions of this work consist of i) the design of a decentralized data sharing solution with accountability and compliance by design and ii) the inclusion of a dynamic consent model for personalized data sharing preferences and for enabling legal compliance mechanisms. We test the scalability of the platform in a real-time environment where a growing number of users access and reuse different datasets. Compared to existing data sharing solutions, LUCE provides transparency over data sharing practices, enables data reuse, and supports regulatory requirements. The experimentation shows that the platform can be scaled for a large number of users.
Blockchain is an essentially distributed database recording all transactions or digital events among participating parties. Each transaction in the records is approved and verified by consensus of the participants in the system that requires solving a hard mathematical puzzle, which is known as proof-of-work. To make the approved records immutable, the mathematical puzzle is not trivial to solve and therefore consumes substantial computing resources. However, it is energy-wasteful to have many computational nodes installed in the blockchain competing to approve the records by just solving a meaningless puzzle. Here, we pose proof-of-work as a reinforcement-learning problem by modeling the blockchain growing as a Markov decision process, in which a learning agent makes an optimal decision over the environment's state, whereas a new block is added and verified. Specifically, we design the block verification and consensus mechanism as a deep reinforcement-learning iteration process. As a result, our method utilizes the determination of state transition and the randomness of action selection of a Markov decision process, as well as the computational complexity of a deep neural network, collectively to make the blocks not easy to recompute and to preserve the order of transactions, while the blockchain nodes are exploited to train the same deep neural network with different data samples (state-action pairs) in parallel, allowing the model to experience multiple episodes across computing nodes but at one time. Our method is used to design the next generation of public blockchain networks, which has the potential not only to spare computational resources for industrial applications but also to encourage data sharing and AI model design for common problems.
Ali Dorri, Clemence Roulin, Shantanu Pal, Sarah Baalbaki · 6 authors
In recent years, blockchain technology has received tremendous attention. Blockchain users are known by a changeable public key (PK) that introduces a level of anonymity; however, studies have shown that anonymized transactions can be linked to deanonymize the users. Most of the existing studies on user deanonymization focus on monetary applications; however, the blockchain has received extensive attention in nonmonetary applications such as the Internet of Things (IoT). In this article, we study the impact of deanonymization on the IoT-based blockchain. We populate a blockchain with data of smart home devices and then apply machine learning algorithms in an attempt to classify the transactions to a particular device that, in turn, risks the privacy of the users. Two types of attack models are defined: 1) informed attacks: where attackers know the type of devices installed in a smart home and 2) blind attacks: where attackers do not have this information. We show that machine learning algorithms can successful classify the transactions with 90% accuracy. To enhance the anonymity of the users, we introduce multiple obfuscation methods which include combining multiple packets into a transaction, merging ledgers of multiple devices, and delaying transactions. The implementation results show that these obfuscation methods significantly reduce the attack success rates to 20%–30% and, thus, enhance the user privacy.
Jacques Fleischer, Gregor von Laszewski, Carlos Theran, Yohn Jairo Parra Bautista
In this paper we apply neural networks and Artificial Intelligence (AI) to historical records of high-risk cryptocurrency coins to train a prediction model that guesses their price. This paper's code contains Jupyter notebooks, one of which outputs a timeseries graph of any cryptocurrency price once a CSV file of the historical data is inputted into the program. Another Jupyter notebook trains an LSTM, or a long short-term memory model, to predict a cryptocurrency's closing price. The LSTM is fed the close price, which is the price that the currency has at the end of the day, so it can learn from those values. The notebook creates two sets: a training set and a test set to assess the accuracy of the results. The data is then normalized using manual min-max scaling so that the model does not experience any bias; this also enhances the performance of the model. Then, the model is trained using three layers -- an LSTM, dropout, and dense layer-minimizing the loss through 50 epochs of training; from this training, a recurrent neural network (RNN) is produced and fitted to the training set. Additionally, a graph of the loss over each epoch is produced, with the loss minimizing over time. Finally, the notebook plots a line graph of the actual currency price in red and the predicted price in blue. The process is then repeated for several more cryptocurrencies to compare prediction models. The parameters for the LSTM, such as number of epochs and batch size, are tweaked to try and minimize the root mean square error.
The concept of blockchain has emerged as an effective solution for data-sensitive domains, such as healthcare, financial services, etc., due to its various attributes like immutability, non-repudiation, and availability. Thus, implementation of this technology in various domains rose exponentially; one of such fields is the healthcare supply chain. Managing healthcare supply chain processes effectively is very crucial for the healthcare system. Despite various innovations in the method of treatment methodologies, the healthcare supply chain management system is not up to the mark and lacks efficiency. The traditional healthcare supply chain system is time-consuming and lacks the work synergy among the various stakeholders of the supply chain. Thus, In this paper, we propose a framework based on blockchain smart contracts and decentralized storage to connect all the supply chain stakeholders. Smart contracts in the framework enforce and depict various interactions and transactions among the stakeholders, thus helping to automate these processes, promote transparency, improve efficiency, and minimize service time. The preliminary results show that the proposed framework is more efficient, secure, and economically feasible.
Byzantine state-machine replication (SMR) ensures the consistency of replicated state in the presence of malicious replicas and lies at the heart of the modern blockchain technology. Byzantine SMR protocols often guarantee safety under all circumstances and liveness only under synchrony. However, guaranteeing liveness even under this assumption is nontrivial. So far we have lacked systematic ways of incorporating liveness mechanisms into Byzantine SMR protocols, which often led to subtle bugs. To close this gap, we introduce a modular framework to facilitate the design of provably live and efficient Byzantine SMR protocols. Our framework relies on a view abstraction generated by a special SMR synchronizer primitive to drive the agreement on command ordering. We present a simple formal specification of an SMR synchronizer and its bounded-space implementation under partial synchrony. We also apply our specification to prove liveness and analyze the latency of three Byzantine SMR protocols via a uniform methodology. In particular, one of these results yields what we believe is the first rigorous liveness proof for the algorithmic core of the seminal PBFT protocol.
One of the most important tasks in scientific publishing is the articles' evaluation via the editorial board and the reviewers' community. Additionally, in scientific publishing great concern exists regarding the peer-review process and how it can be further optimised to decrease the time from submission to the first decision, as well as increase the objectivity of the reviewers' remarks ensuring that no bias or human error exists in the reviewing process. In order to address this issue, our article suggests a novice cloud framework for manuscript submission based on blockchain technology that further enhances the anonymity between authors and reviewers alike. Our method covers the whole spectrum of current submission systems capabilities, but it also provides a decentralised solution using open-source tools such as Java Spring that enhance the anonymity of the reviewing process.
Jakub Sliwinski, Yann Vonlanthen, Roger Wattenhofer
Digital money can be implemented efficiently by avoiding consensus. However, no-consensus implementations have drawbacks, as they cannot support smart contracts, and (even more fundamentally) they cannot deal with conflicting transactions. We present a novel protocol that combines the benefits of an asynchronous, broadcast-based digital currency, with the capacity to perform consensus. This is achieved by selectively performing consensus a posteriori, i.e., only when absolutely necessary. Our on-demand consensus comes at the price of restricting the Byzantine participants to be less than a one-fifth minority in the system, which is the optimal threshold. We formally prove the correctness of our system and present an open-source implementation, which inherits many features from the Ethereum ecosystem.
Kadir Korkmaz, Joachim Bruneau‐Queyreix, Sonia Ben Mokhtar, Laurent Réveillère
Most of today's online services (e.g., social networks, search engines, market places) are centralized, which is recognized as unsatisfactory by a majority of users for various reasons (e.g., centralized governance, censorship, loss of control over personal data). Blockchain technologies promise a new Web revolution (Web 3.0) through the decentralization of online services. However, one of the key limitations for this revolution to happen at a planetary scale is the poor performance of today's blockchains. We propose in this paper ALDER, a solution for unlocking the performance of off-the-shelf leader-based blockchains by multiplexing their consensus protocol. Our solution leverages the existence of multiple potential leaders to alleviate the bottleneck that exists at different levels of consensus protocols. To illustrate the benefits it brings to Blockchain performance, we apply ALDER to three representative blockchains, namely Algorand (Proof-of-Stake), RapidChain (Sharding-based) and Bitcoin (Proof-of-Work). Our evaluation, involving up to 10,000 nodes deployed on 100 physical machines, shows that using ALDER can provide up to a 300% improvement in both throughput and latency reduction.
Blockchains use peer-to-peer networks for disseminating information among peers, but these networks currently do not have any provable guarantees for desirable properties such as Byzantine fault tolerance, good connectivity and small diameter. This is not just a theoretical problem, as recent works have exploited unsafe peer connection policies and weak network synchronization to mount partitioning attacks on Bitcoin. Cryptocurrency blockchains are safety critical systems, so we need principled algorithms to maintain their networks. Our key insight is that we can leverage the blockchain itself to share information among the peers, and thus simplify the network maintenance process. Given that the peers have restricted computational resources, and at most a constant fraction of them are Byzantine, we provide communication-efficient protocols to maintain a hypercubic network for blockchains, where peers can join and leave over time. Interestingly, we discover that our design can \emph{recover} from substantial adversarial failures. Moreover, these properties hold despite significant churn. A key contribution is a secure mechanism for joining the network that uses the blockchain to help new peers to contact existing peers. Furthermore, by examining how peers join the network, i.e., the "bootstrapping service," we give a lower bound showing that (within log factors) our network tolerates the maximum churn rate possible. In fact, we can give a lower bound on churn for any fully distributed service that requires connectivity.
It is interesting but difficult and challenging to study Ethereum with multiple mining pools. One of the main difficulties comes from not only how to represent such a general tree with multiple block branches (or sub-chains) related to the multiple mining pools, but also how to analyze a multi-dimensional stochastic system due to the mining competition among the multiple mining pools. In this paper, we first set up a mathematical representation for the tree with multiple block branches. Then we provide a block classification of Ethereum: Regular blocks (in the main chain), orphan blocks, uncle blocks, stale blocks, and nephew blocks, and give some key ratios and probabilities of generating the different types of blocks by applying the law of large numbers. Based on this, we further discuss the growth rate of blockchain and the reward allocation among the multiple mining pools through applying the renewal reward theorem. Finally, we use some simulation experiments to verify our theoretical results, and show that the approximate computation approaches developed, such as the key ratios and probabilities, the long-term growth rate of blockchain, and the long-term reward allocation (rate) among the multiple mining pools, can have a faster convergence. Therefore, we provide a powerful tool for observing and understanding the influence of the selfish mining attacks on the performance of Ethereum with multiple mining pools. We believe that the methodology and results developed in this paper will shed light on the study of Ethereum with multiple mining pools, such that a series of promising research can be inspired potentially.
Computational task offloading based on edge computing can deal with the performance bottleneck of traditional cloud-based systems for Internet of things (IoT). To further optimize computing efficiency and resource allocation, collaborative offloading has been put forward to enable the offloading from edge devices to IoT terminal devices. However, there still lack incentive mechanisms to encourage participants to take over tasks from others. To counter this situation, this paper proposes a distributed computational resource trading strategy addressing multiple preferences of IoT users. Unlike most existing works, the objective of our trading strategy comprehensively considers different satisfaction degrees with task delay, energy consumption, price, and user reputation of both requesters and collaborators. The system design uses blockchain to enhance the decentralization, security, and automation. Compared with the trading method based on classical double auction matching mechanism, our trading strategy has more tasks offloaded and executed, and the trading results are friendlier to collaborators with good reputation.
Amir Afaq, Zeeshan Ahmed, Noman Haider, Muhammad Ali Imran
Federated Learning (FL) provides privacy preservation by allowing the model training at edge devices without the need of sending the data from edge to a centralized server. FL has distributed the implementation of ML. Another variant of FL which is well suited for the Internet of Things (IoT) is known as Collaborated Federated Learning (CFL), which does not require an edge device to have a direct link to the model aggregator. Instead, the devices can connect to the central model aggregator via other devices using them as relays. Although, FL and CFL protect the privacy of edge devices but raises security challenges for a centralized server that performs model aggregation. The centralized server is prone to malfunction, backdoor attacks, model corruption, adversarial attacks and external attacks. Moreover, edge device to centralized server data exchange is not required in FL and CFL, but model parameters are sent from the model aggregator (global model) to edge devices (local model), which is still prone to cyber-attacks. These security and privacy concerns can be potentially addressed by Blockchain technology. The blockchain is a decentralized and consensus-based chain where devices can share consensus ledgers with increased reliability and security, thus significantly reducing the cyberattacks on an exchange of information. In this work, we will investigate the efficacy of blockchain-based decentralized exchange of model parameters and relevant information among edge devices and from a centralized server to edge devices. Moreover, we will be conducting the feasibility analysis for blockchain-based CFL models for different application scenarios like the internet of vehicles, and the internet of things. The proposed study aims to improve the security, reliability and privacy preservation by the use of blockchain-powered CFL.
Beyond Visual Line of Sight operation enables drones to surpass the limits imposed by the reach and constraints of their operator's eyes. It extends their range and, as such, productivity, and profitability. Drones operating BVLOS include a variety of highly sensitive assets and information that could be subject to unintentional or intentional security vulnerabilities. As a solution, blockchain-based services could enable secure and trustworthy exchange and storage of related data. They also allow for traceability of exchanges and perform synchronization with other nodes in the network. However, most of the blockchain-based approaches focus on the network and the protocol aspects of drone systems. Few studies focus on the architectural level of on-chip compute platforms of drones. Based on this observation, the contribution of this paper is twofold: (1) a generic blockchain-based service architecture for on-chip compute platforms of drones, and (2) a concrete example realization of the proposed generic architecture.
Unmanned Aerial Vehicles (UAVs), also known as drones, have exploded in every segment present in todays business industry. They have scope in reinventing old businesses, and they are even developing new opportunities for various brands and franchisors. UAVs are used in the supply chain, maintaining surveillance and serving as mobile hotspots. Although UAVs have potential applications, they bring several societal concerns and challenges that need addressing in public safety, privacy, and cyber security. UAVs are prone to various cyber-attacks and vulnerabilities; they can also be hacked and misused by malicious entities resulting in cyber-crime. The adversaries can exploit these vulnerabilities, leading to data loss, property, and destruction of life. One can partially detect the attacks like false information dissemination, jamming, gray hole, blackhole, and GPS spoofing by monitoring the UAV behavior, but it may not resolve privacy issues. This paper presents secure communication between UAVs using blockchain technology. Our approach involves building smart contracts and making a secure and reliable UAV adhoc network. This network will be resilient to various network attacks and is secure against malicious intrusions.
A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, the mempool, and exchanges, we study the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting times, and consensus security. Our results show that EIP-1559 improves the user experience by mitigating intrablock differences in the gas price paid and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we find that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFMs.
Péter Garamvölgyi, Yuxi Liu, Dong Zhou, Fan Long · 5 authors
Traditional public blockchain systems typically had very limited transaction throughput because of the bottleneck of the consensus protocol itself. With recent advances in consensus technology, the performance limit has been greatly lifted, typically to thousands of transactions per second. With this, transaction execution has become a new performance bottleneck. Exploiting parallelism in transaction execution is a clear and direct way to address this and to further increase transaction throughput. Although some recent literature introduced concurrency control mechanisms to execute smart contract transactions in parallel, the reported speedup that they can achieve is far from ideal. The main reason is that the proposed parallel execution mechanisms cannot effectively deal with the conflicts inherent in many blockchain applications. In this work, we thoroughly study the historical transaction execution traces in Ethereum. We observe that application-inherent conflicts are the major factors that limit the exploitable parallelism during execution. We propose to use partitioned counters and special commutative instructions to break up the application conflict chains in order to maximize the potential speedup. When we evaluated the maximum parallel speedup achievable, these techniques doubled this limit to an 18x overall speedup compared to serial execution, thus approaching the optimum. We also propose OCC-DA, an optimistic concurrency control scheduler with deterministic aborts, which makes it possible to use OCC scheduling in public blockchain settings.
Minghui Xu, Zongrui Zou, Ye Cheng, Qin Hu · 6 authors
Decentralized learning involves training machine learning models over remote mobile devices, edge servers, or cloud servers while keeping data localized. Even though many studies have shown the feasibility of preserving privacy, enhancing training performance or introducing Byzantine resilience, but none of them simultaneously considers all of them. Therefore we face the following problem: \textit{how can we efficiently coordinate the decentralized learning process while simultaneously maintaining learning security and data privacy?} To address this issue, in this paper we propose SPDL, a blockchain-secured and privacy-preserving decentralized learning scheme. SPDL integrates blockchain, Byzantine Fault-Tolerant (BFT) consensus, BFT Gradients Aggregation Rule (GAR), and differential privacy seamlessly into one system, ensuring efficient machine learning while maintaining data privacy, Byzantine fault tolerance, transparency, and traceability. To validate our scheme, we provide rigorous analysis on convergence and regret in the presence of Byzantine nodes. We also build a SPDL prototype and conduct extensive experiments to demonstrate that SPDL is effective and efficient with strong security and privacy guarantees.