Valentin Zieglmeier, Gabriel Loyola Daiqui, Alexander Pretschner
Employee data can be used to facilitate work, but their misusage may pose risks for individuals. Inverse transparency therefore aims to track all usages of personal data, allowing individuals to monitor them to ensure accountability for potential misusage. This necessitates a trusted log to establish an agreed-upon and non-repudiable timeline of events. The unique properties of blockchain facilitate this by providing immutability and availability. For power asymmetric environments such as the workplace, permissionless blockchain is especially beneficial as no trusted third party is required. Yet, two issues remain: (1) In a decentralized environment, no arbiter can facilitate and attest to data exchanges. Simple peer-to-peer sharing of data, conversely, lacks the required non-repudiation. (2) With data governed by privacy legislation such as the GDPR, the core advantage of immutability becomes a liability. After a rightful request, an individual's personal data need to be rectified or deleted, which is impossible in an immutable blockchain. To solve these issues, we present Kovacs, a decentralized data exchange and usage logging system for inverse transparency built on blockchain. Its new-usage protocol ensures non-repudiation, and therefore accountability, for inverse transparency. Its one-time pseudonym generation algorithm guarantees unlinkability and enables proof of ownership, which allows data subjects to exercise their legal rights regarding their personal data. With our implementation, we show the viability of our solution. The decentralized communication impacts performance and scalability, but exchange duration and storage size are still reasonable. More importantly, the provided information security meets high requirements. We conclude that Kovacs realizes decentralized inverse transparency through secure and GDPR-compliant use of permissionless blockchain.
Ahmad J. Alkhodair, Saraju P. Mohanty, Elias Kougianos
Distributed Ledger Technology (DLT) has been introduced using the most common consensus algorithm either for an electronic cash system or a decentralized programmable assets platform which provides general services. Most established reliable networks are unsuitable for all applications such as smart cities applications, and, in particular, Internet of Things (IoT) and Cyber Physical Systems (CPS) applications. The purpose of this paper is to provide a suitable DLT for IoT and CPS that could satisfy their requirements. The proposed work has been designed based on the requirements of Cyber Physical Systems. FlexiChain is proposed as a layer zero network that could be formed from independent blockchains. Also, NodeChain has been introduced to be a distributed (Unique ID) UID aggregation vault to secure all nodes' UIDs. Moreover, NodeChain is proposed to serve mainly FlexiChain for all node security requirements. NodeChain targets the security and integrity of each node. Also, the linked UIDs create a chain of narration that keeps track not merely for assets but also for who authenticated the assets. The security results present a higher resistance against four types of attacks. Furthermore, the strength of the network is presented from the early stages compared to blockchain and central authority. FlexiChain technology has been introduced to be a layer zero network for all CPS decentralized applications taking into accounts their requirements. FlexiChain relies on lightweight processing mechanisms and creates other methods to increase security.
Blockchain has been used in several domains. However, this technology still has major limitations that are largely related to one of its core components, namely the consensus protocol/algorithm. Several solutions have been proposed in literature and some of them are based on the use of Machine Learning (ML) methods. The ML-based consensus algorithms usually waste the work done by the (contributing/participating) nodes, as only winners' ML models are considered/used, resulting in low energy efficiency. To reduce energy waste and improve scalability, this paper proposes an AI-enabled consensus algorithm (named AICons) driven by energy preservation and fairness of rewarding nodes based on their contribution. In particular, the local ML models trained by all nodes are utilised to generate a global ML model for selecting winners, which reduces energy waste. Considering the fairness of the rewards, we innovatively designed a utility function for the Shapley value evaluation equation to evaluate the contribution of each node from three aspects, namely ML model accuracy, energy consumption, and network bandwidth. The three aspects are combined into a single Shapley value to reflect the contribution of each node in a blockchain system. Extensive experiments were carried out to evaluate fairness, scalability, and profitability of the proposed solution. In particular, AICons has an evenly distributed reward-contribution ratio across nodes, handling 38.4 more transactions per second, and allowing nodes to get more profit to support a bigger network than the state-of-the-art schemes.
This master thesis deals with Blockchain Technology in mobile turn based peer to peer games. First, it investigates the capabilities of Blockchain Technology to be used for gaming applications. In this regard, among others, Proof-of-Mechanisms, Vote-based Consensus and several Performance Improvements are described. Second, several smart contracts are introduced to show the general feasibility of turn based games hosted on Blockchain Technology. More specific, Hidden transactions, Randomization, Piles of Cards, Fog of War elements, Data allocation improvements and other smart contracts are specified. Third, a special Proof-of-Turn consensus mechanism, based on the Blockchain Technology, is defined to enable game publishers to cut costs in the means of their provided game servers. Herein, Byzantine Fault Tolerance, Peering, the CAP Theorem, Interoperability among other characteristics are covered. Last, these measures shall additionally raise the trust level among the players in mobile turn based games.
Andrea Canciani, Claudio Felicioli, Andrea Lisi, Fabio Severino
We propose a new approach, termed Hybrid DLT, to address a broad range of industrial use cases where certain properties of both private and public DLTs are valuable, while other properties may be unnecessary or detrimental. The Hybrid DLT approach involves a system where private ledgers, with limited data block dissemination, are collaboratively created by nodes within a private network. The Notary, a publicly auditable authoritative component, maintains a single, official, coherent history for each private ledger without requiring access to data blocks. This is achieved by leveraging a public DLT solution to render the ledger histories tamper-proof, consequently providing tamper-evidence for ledger data disclosed to external actors. We present Traent Hybrid Blockchain, a commercial implementation of the Hybrid DLT approach: a real-time, data-intensive collaboration system for organizations seeking immutable data while also needing to comply with the European General Data Protection Regulation (GDPR).
Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and its crucial components. The macro-level exploration provides a big picture on the endeavors made by blockchain professionals over the years to enhance the blockchain performance while the micro-level investigation details the blockchain building blocks for deep technology comprehension. More specifically, this article introduces a general modular blockchain analytic framework that decomposes a blockchain system into interacting modules and then examines the major modules to cover the essential blockchain components of network, consensus, and distributed ledger at the micro-level. The framework as well as the modular analysis jointly build a foundation for designing scalable, flexible, and application-adaptive blockchains that can meet diverse requirements. Additionally, this article explores popular technologies that can be integrated with blockchain to expand functionality and highlights major challenges. Such a study provides critical insights to overcome the obstacles in designing novel blockchain systems and facilitates the further development of blockchain as a digital infrastructure to service new applications.
Nastaran Abadi Khooshemehr, Mohammad Ali Maddah-Ali
Coded computing has proved to be useful in distributed computing. We have observed that almost all coded computing systems studied so far consider a setup of one master and some workers. However, recently emerging technologies such as blockchain, internet of things, and federated learning introduce new requirements for coded computing systems. In these systems, data is generated in a distributed manner, so central encoding/decoding by a master is not feasible and scalable. This paper presents a fully distributed coded computing system that consists of $k\in\mathbb{N}$ data owners and $N\in\mathbb{N}$ workers, where data owners employ workers to do some computations on their data, as specified by a target function $f$ of degree $d\in\mathbb{N}$. As there is no central encoder, workers perform encoding themselves, prior to computation phase. The challenge in this system is the presence of adversarial data owners that do not know the data of honest data owners but cause discrepancies by sending different data to different workers, which is detrimental to local encodings in workers. There are at most $β\in\mathbb{N}$ adversarial data owners, and each sends at most $v\in\mathbb{N}$ different versions of data. Since the adversaries and their possibly colluded behavior are not known to workers and honest data owners, workers compute tags of their received data, in addition to their main computational task, and send them to data owners to help them in decoding. We introduce a tag function that allows data owners to partition workers into sets that previously had received the same data from all data owners. Then, we characterize the fundamental limit of the system, $t^*$, which is the minimum number of workers whose work can be used to correctly calculate the desired function of data of honest data owners. We show that $t^*=v^βd(K-1)+1$, and present converse and achievable proofs.
Many applications, e.g., digital twins, rely on sensing data from Internet of Things (IoT) networks, which is used to infer event(s) and initiate actions to affect an environment. This gives rise to concerns relating to data integrity and provenance. One possible solution to address these concerns is to employ blockchain. However, blockchain has high resource requirements, thereby making it unsuitable for use on resource-constrained IoT devices. To this end, this paper proposes a novel approach, called two-layer directed acyclic graph (2LDAG), whereby IoT devices only store a digital fingerprint of data generated by their neighbors. Further, it proposes a novel proof-of-path (PoP) protocol that allows an operator or digital twin to verify data in an on-demand manner. The simulation results show 2LDAG has storage and communication cost that is respectively two and three orders of magnitude lower than traditional blockchain and also blockchains that use a DAG structure. Moreover, 2LDAG achieves consensus even when 49\% of nodes are malicious.
With the rising emergence of decentralized and opportunistic approaches to machine learning, end devices are increasingly tasked with training deep learning models on-devices using crowd-sourced data that they collect themselves. These approaches are desirable from a resource consumption perspective and also from a privacy preservation perspective. When the devices benefit directly from the trained models, the incentives are implicit - contributing devices' resources are incentivized by the availability of the higher-accuracy model that results from collaboration. However, explicit incentive mechanisms must be provided when end-user devices are asked to contribute their resources (e.g., computation, communication, and data) to a task performed primarily for the benefit of others, e.g., training a model for a task that a neighbor device needs but the device owner is uninterested in. In this project, we propose a novel blockchain-based incentive mechanism for completely decentralized and opportunistic learning architectures. We leverage a smart contract not only for providing explicit incentives to end devices to participate in decentralized learning but also to create a fully decentralized mechanism to inspect and reflect on the behavior of the learning architecture.
Data synchronization in decentralized storage systems is essential to guarantee sufficient redundancy to prevent data loss. We present SNIPS, the first succinct proof of storage algorithm for synchronizing storage peers. A peer constructs a proof for its stored chunks and sends it to verifier peers. A verifier queries the proof to identify and subsequently requests missing chunks. The proof is succinct, supports membership queries, and requires only a few bits per chunk. We evaluated our SNIPS algorithm on a cluster of 1000 peers running Ethereum Swarm. Our results show that SNIPS reduces the amount of synchronization data by three orders of magnitude compared to the state-of-the-art. Additionally, creating and verifying a proof is linear with the number of chunks and typically requires only tens of microseconds per chunk. These qualities are vital for our use case, as we envision running SNIPS frequently to maintain sufficient redundancy consistently.
In contrast to proof-of-work replication, Byzantine quorum systems maintain consistency across replicas with higher throughput modest energy consumption, and deterministic liveness guarantees. If complemented with heterogeneous trust and open membership, they have the potential to serve as blockchains backbone. This paper presents a general model of heterogeneous quorum systems where each participant can declare its own quorums, and captures the consistency, availability and inclusion properties of these systems. In order to support open membership, it then presents reconfiguration protocols for heterogeneous quorum systems including joining and leaving of a process, and adding and removing of a quorum, and further, proves their correctness in the face of Byzantine attacks. The design of the protocols is informed by the trade-offs that the paper proves for the properties that reconfigurations can preserve. The paper further presents a graph characterization of heterogeneous quorum systems, and its application for reconfiguration optimization.
The problem of data synchronization arises in networked applications that require some measure of consistency. Indeed data synchronization approaches have demonstrated a significant potential for improving performance in various applications ranging from distributed ledgers to fog-enabled storage offloading for IoT. Although several protocols for data sets synchronization have been proposed over the years, there is currently no widespread utility implementing them, unlike the popular Rsync utility available for file synchronization. To that end, we describe a new middleware called GenSync that abstracts the subtleties of the state-of-the-art data synchronization protocols, allows users to choose protocols based on a comparative evaluation under realistic system conditions, and seamlessly integrate protocols in existing applications through a public API. We showcase GenSync through a case study, in which we integrate it into one of the world's largest wireless emulators and compare the performance of its included protocols.
Novak Boškov, Şevval Şimşek, Ari Trachtenberg, David Starobinski
Synchronization of transaction pools (mempools) has shown potential for improving the performance and block propagation delay of state-of-the-art blockchains. Indeed, various heuristics have been proposed in the literature to this end, all of which incorporate exchanges of unconfirmed transactions into their block propagation protocol. In this work, we take a different approach, maintaining transaction synchronization outside (and independently) of the block propagation channel. In the process, we formalize the synchronization problem within a graph theoretic framework and introduce a novel algorithm (SREP - Set Reconciliation-Enhanced Propagation) with quantifiable guarantees. We analyze the algorithm's performance for various realistic network topologies, and show that it converges on any connected graph in a number of steps that is bounded by the diameter of the graph. We confirm our analytical findings through extensive simulations that include comparison with MempoolSync, a recent approach from the literature. Our simulations show that SREP incurs reasonable overall bandwidth overhead and, unlike MempoolSync, scales gracefully with the size of the network.
Alexander Mozeika, Mohammad M. Jalalzai, Marcin P. Pawlowski
Blockchains facilitate decentralization, security, identity, and data management in cyber-physical systems. However, consensus protocols used in blockchains are prone to high message and computational complexity costs and are not suitable to be used in IoT. One way to reduce message complexity is to randomly assign network nodes into committees or shards. Keeping committee sizes small is then desirable in order to achieve lower message complexity, but this comes with a penalty of reduced reliability as there is a higher probability that a large number of faulty nodes will end up in a committee. In this work, we study the problem of estimating a probability of a failure in randomly sharded networks. We provide new results and improve existing bounds on the failure probability. Thus, our framework also paves the way to reduce committee sizes without reducing reliability.
Ervin Moore, Ahmed Imteaj, Shabnam Rezapour, M. Hadi Amini
Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users' sensitive data, the performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of FL process, the integration of blockchain for FL environments has drawn prolific attention from the people of academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristic. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms that are suitable for FL process and discuss their trade-offs for a limited resource budget. Further, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role to block those cyber attacks. To this end, we highlight some potential solutions towards the coupling of blockchain and federated learning that can offer high levels of reliability, data privacy, and distributed computing performance.
Joāo Otávio Chervinski, Diego Kreutz, Xiwei Xu, Jiangshan Yu
With the increasing demand for communication between blockchains, improving the performance of cross-chain communication protocols becomes an emerging challenge. We take a first step towards analyzing the limitations of cross-chain communication protocols by comprehensively evaluating Cosmos Network's Inter-Blockchain Communication Protocol. To achieve our goal we introduce a novel framework to guide empirical evaluations of cross-chain communication protocols. We implement an instance of our framework as a tool to evaluate the IBC protocol. Our findings highlight several challenges, such as high transaction confirmation latency, bottlenecks in the blockchain's RPC implementation and concurrency issues that hinder the scalability of the cross-chain message relayer. We also demonstrate how to reduce the time required to complete cross-chain transfers by up to 70% when submitting large amounts of transfers. Finally, we discuss challenges faced during deployment with the objective of contributing to the development and advancement of cross-chain communication.
Christian Berger, Signe Schwarz-Rüsch, Arne Vogel, Kai Bleeke · 7 authors
With the advancement of blockchain systems, many recent research works have proposed distributed ledger technology~(DLT) that employs Byzantine fault-tolerant~(BFT) consensus protocols to decide which block to append next to the ledger. Notably, BFT consensus can offer high performance, energy efficiency, and provable correctness properties, and it is thus considered a promising building block for creating highly resilient and performant blockchain infrastructures. Yet, a major ongoing challenge is to make BFT consensus applicable to large-scale environments. A large body of recent work addresses this challenge by developing novel ideas to improve the scalability of BFT consensus, thus opening the path for a new generation of BFT protocols tailored to the needs of blockchain. In this survey, we create a systematization of knowledge about the novel scalability-enhancing techniques that state-of-the-art BFT consensus protocols use. For our comparison, we closely analyze the efforts, assumptions, and trade-offs these protocols make.
Decentralized applications rely on non-centralized technical infrastructures and coordination principles. Without trusted third parties, their execution is not controlled by entities exercising centralized coordination but is instead realized through technologies supporting distribution such as blockchains and serverless computing. Executing decentralized applications with these technologies, however, is challenging due to the limited transparency and insight in the execution, especially when involving centralized cloud platforms. This paper extends an approach for execution and instance tracking on blockchains and cloud platforms permitting distributed parties to observe the instances and states of executable models. The approach is extended with (1.) a metamodel describing the concepts for instance tracking on cloud platforms independent of concrete models or implementation, (2.) a multidimensional data model realizing the concepts accordingly, permitting the verifiable storage, tracking, and analysis of execution states for distributed parties, and (3.) an implementation on the Ethereum blockchain and Amazon Web Services (AWS) using state machine models. Towards supporting decentralized applications with high scalability and distribution requirements, the approach establishes a consistent view on instances for distributed parties to track and analyze the execution along multiple dimensions such as specific clients and execution engines.
Abstract We introduce semitopology, a generalization of point-set topology that removes the restriction that intersections of open sets need necessarily be open. The intuition is that points represent participants in a decentralized system, and open sets represent collections of participants that collectively have the authority to collaborate to update their local state; we call this an actionable coalition. Examples of actionable coalition include: majority stakes in proof-of-stake blockchains; communicating peers in peer-to-peer networks; and even pedestrians working together to not bump into one another in the street. Where actionable coalitions exist, they have in common that collaborations are local (updating the states of the participants in the coalition, but not immediately those of the whole system); collaborations are voluntary (up to and including breaking rules); participants may be heterogeneous in their computing power or in their goals (not all pedestrians want to go to the same place); participants can choose with whom to collaborate; and they are not assumed subject to permission or synchronization by a central authority. We develop a topology-flavoured mathematics that goes some way to explaining how and why these complex decentralized systems can exhibit order, and gives us new ways to understand existing practical implementations. Semitopology is also interesting in and of itself, having a rich and interesting theory that quickly deviates from standard accounts on topological spaces. It soon becomes clear that the most interesting semitopologies are rather ill-behaved from the usual viewpoint, as they are never Hausdorff. A notion of ‘transitive open sets’ (topens) becomes central to the story, as topens define subsets of participants who should decide the same value in a distributed system that tries to achieve consensus, and points are called ‘regular’ when they have a topen neighbourhood. The theory is then further developed by introducing intertwined points, closures, closed sets and two interesting characterizations of regularity.
For Nakamoto's longest-chain consensus protocol, whose proof-of-work (PoW) and proof-of-stake (PoS) variants power major blockchains such as Bitcoin and Cardano, we revisit the classic problem of the security--performance tradeoff: Given a network of nodes with finite communication- and computation-resources, against what fraction of adversary power is Nakamoto consensus (NC) secure for a given block production rate? State-of-the-art analyses of NC fail to answer this question, because their bounded-delay model does not capture the rate limits to nodes' processing of blocks, which cause congestion when blocks are released in quick succession. We develop a new analysis technique to prove a refined security--performance tradeoff for PoW NC in a bounded-capacity model. In this model, we show that, in contrast to the classic bounded-delay model, Nakamoto's private attack is no longer the worst attack, and a new attack we call the teasing strategy, that exploits congestion, is strictly worse. In PoS, equivocating blocks can exacerbate congestion, making traditional PoS NC insecure except at very low block production rates. To counter such equivocation spamming, we present a variant of PoS NC we call Blanking NC (BlaNC), which achieves the same resilience as PoW NC.
The Hyperledger Fabric is well known and the most prominent enterprise-grade permissioned blockchain. The architecture of the Hyperledger Fabric introduces a new architecture paradigm of simulate-order-validate and pluggable architecture, allowing a greater level of customization where one of the critical components is the world state database, which is responsible for capturing the snapshot of the blockchain application state. Hyperledger Fabric manages the state with the key-value database abstraction and peer updates it after transactions have been validated and read from the state during simulation. Therefore, providing good performance during reading and writing impacts the system's overall performance. Currently, Hyperledger Fabric supports two different implementations of the state database. One is LevelDB, the embedded DB based on LSM trees and CouchDB. In this study, we would like to focus on searching and exploring the alternative implementation of a state database and analyze whenever there are better and more scalable options. We evaluated different databases to be plugged into Hyperledger Fabric, such as RocksDB, Boltdb, and BadgerDB. The study describes how to plug new state databases and performance results based on various workloads.
Linh Thủy Nguyễn, Lam Duc Nguyen, Thong Hoang, H. M. N. Dilum Bandara · 10 authors
The rise of data-sharing platforms, driven by public demand for open data and legislative mandates, has raised several pertinent issues. These encompass uncertainties over data accuracy, provenance and lineage, privacy concerns, consent management, and the lack of equitable incentives for data providers. The advanced nature of blockchain makes it well suited to address these concerns. Yet, the limitations of blockchains, particularly their restricted performance, scalability, and high cost, make them less adept at managing the four “Vs” of big data—volume, variety, velocity, and veracity. As the body of work proposing blockchain-based data-sharing solutions grows, so does the confusion in selecting between these platforms, particularly in terms of sharing mechanisms, services, quality of services, and applications. In this article, we aim to fill this knowledge gap through an in-depth survey of blockchain-based data-sharing architectures and applications. We first identify the key challenges of existing data-sharing techniques and lay out the foundations of blockchains. Our focus then shifts to the intersection of blockchain and data sharing, wherein we aim to clarify the existing landscape and propose a reference architecture for blockchain-based data sharing. Subsequently, we explore various industrial applications of blockchain-based data sharing, spanning healthcare, smart grids, transportation, and decarbonization. For each application, we draw from real-world deployments to present key lessons learned in the implementation of blockchain-based data sharing. Lastly, we shed light on current research challenges and open avenues for further study in this space. This article aims to serve as a comprehensive resource for researchers/practitioners looking to navigate the complex terrain of blockchain-based data-sharing solutions.
Nakamoto consensus has been incredibly influential in enabling robust blockchain systems, and one of its components is the so-called heaviest chain rule (HCR). Within this rule, the calculation of the weight of the chain tip is performed by adding the difficulty threshold value to the previous total difficulty. Current difficulty based weighting systems do not take the intrinsic block weight into account. This paper studies a new mechanism based on entropy differences, named proof of entropy minima (POEM), which incorporates the intrinsic block weight in a manner that significantly reduces the orphan rate of the blockchain while simultaneously accelerating finalization. Finally, POEM helps to understand blockchain as a static time-independent sequence of committed events.
Johannes Rude Jensen, Victor von Wachter, Omri Ross
Multi-block MEV (MMEV) denotes the practice of securing k-consecutive blocks in an attempt at extracting surplus value by manipulating transaction ordering. Following the implementation of pro-poser/builder separation (PBS) on Ethereum, savvy builders can secure consecutive block space by implementing targeted bidding strategies through relays. To estimate the extent to which this practice might be taking place today, we collect data on all bids submitted by builders through relays in the period from the 15th of September (the merge) 2022 until the 31st of January 2023. We hypothesize that builders might secure consecutive blocks in order to deploy sophisticated MMEV strategies, such as creating artificial momentum in Uniswap pools, by withholding and prioritizing transactions from the mempool. In this talk proposal, we present preliminary and non-conclusive results, indicating the builders employ super-linear bidding strategies to secure consecutive block space. We hypothesize that builders act rationally and increase bids only if this is profitable. With this WIP talk proposal, we hope to stimulate an interesting discussion on the feasibility of sophisticated MMEV strategies at SBC23, with the aim of collecting feedback from researchers and practitioners working on MEV.