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CATs: Secure Blockchain Interoperability with Cross-chain Atomic Transactions
Authors:
Andreas Penzkofer,
Franck Cassez
Abstract:
We propose a protocol for cross-chain atomic transactions (CATs), enabling composable atomic execution across different blockchains. The protocol addresses the key interoperability challenge of providing atomicity guarantees in the presence of asynchronous communication and Byzantine actors. It preserves chain autonomy by allowing each blockchain to maintain its own execution model while participa…
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We propose a protocol for cross-chain atomic transactions (CATs), enabling composable atomic execution across different blockchains. The protocol addresses the key interoperability challenge of providing atomicity guarantees in the presence of asynchronous communication and Byzantine actors. It preserves chain autonomy by allowing each blockchain to maintain its own execution model while participating in coordinated cross-chain operations. The design introduces a shared coordination layer involving sequencers, transaction processors, a coordinator, and a confirmation layer which together ensure that either all parts of a CAT succeed or none do.
To prevent unnecessary blocking, we separate transaction execution into accepted and postponed sets, with the coordination layer resolving the outcomes of CATs within a few rounds. We further introduce timeouts and dependency-depth bounds for liveness and mitigation of cascading delays. Our formal analysis establishes strong safety and liveness guarantees and demonstrates that the protocol achieves minimal blocking for independent transactions while ensuring bounded blocking time for dependent transactions. Experimental evaluation shows high CAT success when cross-chain transactions are a modest share of traffic, and characterizes the CAT-lifetime trade-off between success and dependent-transaction latency.
This protocol enables fast, secure, and deterministic atomic cross-chain execution while preserving chain autonomy, providing a foundation for scalable blockchain interoperability solutions.
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Submitted 6 July, 2026;
originally announced July 2026.
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Unsupervised Partner Design Enables Robust Ad-hoc Teamwork
Authors:
Constantin Ruhdorfer,
Matteo Bortoletto,
Victor Oei,
Anna Penzkofer,
Andreas Bulling
Abstract:
We introduce Unsupervised Partner Design (UPD), a population-free multi-agent reinforcement learning method for robust ad-hoc teamwork. UPD generates training partners on-the-fly and selects them adaptively based on a learnability criterion, removing the need for pre-trained partner populations or manual parameter tuning. We show that this simple mechanism enables effective partner diversity and c…
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We introduce Unsupervised Partner Design (UPD), a population-free multi-agent reinforcement learning method for robust ad-hoc teamwork. UPD generates training partners on-the-fly and selects them adaptively based on a learnability criterion, removing the need for pre-trained partner populations or manual parameter tuning. We show that this simple mechanism enables effective partner diversity and can be extended to joint partner-environment selection when a procedural level generator is available. Across Level-Based Foraging, Overcooked-AI, and the Overcooked Generalisation Challenge, UPD consistently achieves strong performance compared to both population-based and population-free baselines. In a human-AI user study, agents trained with UPD achieve higher returns and are rated as more adaptive, more human-like, and less frustrating than all evaluated baseline methods.
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Submitted 7 June, 2026; v1 submitted 8 August, 2025;
originally announced August 2025.
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MultiMediate'24: Multi-Domain Engagement Estimation
Authors:
Philipp Müller,
Michal Balazia,
Tobias Baur,
Michael Dietz,
Alexander Heimerl,
Anna Penzkofer,
Dominik Schiller,
François Brémond,
Jan Alexandersson,
Elisabeth André,
Andreas Bulling
Abstract:
Estimating the momentary level of participant's engagement is an important prerequisite for assistive systems that support human interactions. Previous work has addressed this task in within-domain evaluation scenarios, i.e. training and testing on the same dataset. This is in contrast to real-life scenarios where domain shifts between training and testing data frequently occur. With MultiMediate'…
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Estimating the momentary level of participant's engagement is an important prerequisite for assistive systems that support human interactions. Previous work has addressed this task in within-domain evaluation scenarios, i.e. training and testing on the same dataset. This is in contrast to real-life scenarios where domain shifts between training and testing data frequently occur. With MultiMediate'24, we present the first challenge addressing multi-domain engagement estimation. As training data, we utilise the NOXI database of dyadic novice-expert interactions. In addition to within-domain test data, we add two new test domains. First, we introduce recordings following the NOXI protocol but covering languages that are not present in the NOXI training data. Second, we collected novel engagement annotations on the MPIIGroupInteraction dataset which consists of group discussions between three to four people. In this way, MultiMediate'24 evaluates the ability of approaches to generalise across factors such as language and cultural background, group size, task, and screen-mediated vs. face-to-face interaction. This paper describes the MultiMediate'24 challenge and presents baseline results. In addition, we discuss selected challenge solutions.
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Submitted 29 August, 2024;
originally announced August 2024.
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The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design
Authors:
Constantin Ruhdorfer,
Matteo Bortoletto,
Anna Penzkofer,
Andreas Bulling
Abstract:
We introduce the Overcooked Generalisation Challenge (OGC) - a new benchmark for evaluating reinforcement learning (RL) agents on their ability to cooperate with unknown partners in unfamiliar environments. Existing work typically evaluated cooperative RL only in their training environment or with their training partners, thus seriously limiting our ability to understand agents' generalisation cap…
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We introduce the Overcooked Generalisation Challenge (OGC) - a new benchmark for evaluating reinforcement learning (RL) agents on their ability to cooperate with unknown partners in unfamiliar environments. Existing work typically evaluated cooperative RL only in their training environment or with their training partners, thus seriously limiting our ability to understand agents' generalisation capacity - an essential requirement for future collaboration with humans. The OGC extends Overcooked-AI to support dual curriculum design (DCD). It is fully GPU-accelerated, open-source, and integrated into the minimax DCD benchmark suite. Compared to prior DCD benchmarks, where designers manipulate only minimal elements of the environment, OGC introduces a significantly richer design space: full kitchen layouts with multiple objects that require the designer to account for interaction dynamics between agents. We evaluate state-of-the-art DCD algorithms alongside scalable neural architectures and find that current methods fail to produce agents that generalise effectively to novel layouts and unfamiliar partners. Our results indicate that both agents and curriculum designers struggle with the joint challenge of partner and environment generalisation. These findings establish OGC as a demanding testbed for cooperative generalisation and highlight key directions for future research. We open-source our code.
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Submitted 12 September, 2025; v1 submitted 25 June, 2024;
originally announced June 2024.
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VSA4VQA: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images
Authors:
Anna Penzkofer,
Lei Shi,
Andreas Bulling
Abstract:
While Vector Symbolic Architectures (VSAs) are promising for modelling spatial cognition, their application is currently limited to artificially generated images and simple spatial queries. We propose VSA4VQA - a novel 4D implementation of VSAs that implements a mental representation of natural images for the challenging task of Visual Question Answering (VQA). VSA4VQA is the first model to scale…
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While Vector Symbolic Architectures (VSAs) are promising for modelling spatial cognition, their application is currently limited to artificially generated images and simple spatial queries. We propose VSA4VQA - a novel 4D implementation of VSAs that implements a mental representation of natural images for the challenging task of Visual Question Answering (VQA). VSA4VQA is the first model to scale a VSA to complex spatial queries. Our method is based on the Semantic Pointer Architecture (SPA) to encode objects in a hyperdimensional vector space. To encode natural images, we extend the SPA to include dimensions for object's width and height in addition to their spatial location. To perform spatial queries we further introduce learned spatial query masks and integrate a pre-trained vision-language model for answering attribute-related questions. We evaluate our method on the GQA benchmark dataset and show that it can effectively encode natural images, achieving competitive performance to state-of-the-art deep learning methods for zero-shot VQA.
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Submitted 6 May, 2024;
originally announced May 2024.
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Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning
Authors:
Anna Penzkofer,
Simon Schaefer,
Florian Strohm,
Mihai Bâce,
Stefan Leutenegger,
Andreas Bulling
Abstract:
While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased sample efficiency by incorporating information inherent to the structure of the decision problem but at the cost of having to discover or use human-annotated sub-goals that guide the lear…
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While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased sample efficiency by incorporating information inherent to the structure of the decision problem but at the cost of having to discover or use human-annotated sub-goals that guide the learning process. We show that intentions of human players, i.e. the precursor of goal-oriented decisions, can be robustly predicted from eye gaze even for the long-horizon sparse rewards task of Montezuma's Revenge - one of the most challenging RL tasks in the Atari2600 game suite. We propose Int-HRL: Hierarchical RL with intention-based sub-goals that are inferred from human eye gaze. Our novel sub-goal extraction pipeline is fully automatic and replaces the need for manual sub-goal annotation by human experts. Our evaluations show that replacing hand-crafted sub-goals with automatically extracted intentions leads to a HRL agent that is significantly more sample efficient than previous methods.
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Submitted 20 June, 2023;
originally announced June 2023.
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TangleSim: An Agent-based, Modular Simulator for DAG-based Distributed Ledger Technologies
Authors:
Bing-Yang Lin,
Daria Dziubałtowska,
Piotr Macek,
Andreas Penzkofer,
Sebastian Müller
Abstract:
DAG-based DLTs allow for parallel, asynchronous writing access to a ledger. Consequently, the perception of the most recent blocks may differ considerably between nodes, and the underlying network properties of the P2P layer have a direct impact on the performance of the protocol. Moreover, the stronger inter-dependencies of several core components demand a more complex and complete approach to st…
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DAG-based DLTs allow for parallel, asynchronous writing access to a ledger. Consequently, the perception of the most recent blocks may differ considerably between nodes, and the underlying network properties of the P2P layer have a direct impact on the performance of the protocol. Moreover, the stronger inter-dependencies of several core components demand a more complex and complete approach to studying such DLTs. This paper presents an agent-based, open-sourced simulator for large-scale networks that implement the leaderless Tangle 2.0 consensus protocol. Its scope includes modelling the underlying peer-to-peer communication with network topology, package loss, heterogeneous latency, the gossip protocol with reliable broadcast qualities, the underlying DAG-based data structure, and the consensus protocol. The simulator allows us to explore the performance of the protocol in different network environments, as well as different attack scenarios.
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Submitted 2 May, 2023;
originally announced May 2023.
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Mitigation of liveness attacks in DAG-based ledgers
Authors:
Darcy Camargo,
Andreas Penzkofer,
Sebastian Müller,
William Sanders
Abstract:
The robust construction of the ledger data structure is an essential ingredient for the safe operation of a distributed ledger. While in traditional linear blockchain systems, permission to append to the structure is leader-based, in Directed Acyclic Graph-based ledgers, the writing access can be organised leaderless. However, this leaderless approach relies on fair treatment of non-referenced blo…
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The robust construction of the ledger data structure is an essential ingredient for the safe operation of a distributed ledger. While in traditional linear blockchain systems, permission to append to the structure is leader-based, in Directed Acyclic Graph-based ledgers, the writing access can be organised leaderless. However, this leaderless approach relies on fair treatment of non-referenced blocks, i.e. tips, by honest block issuers.
We study the impact of a deviation from the standard tip selection by a subset of block issuers with the aim of halting the confirmation of honest blocks entirely. We provide models on this so-called orphanage of blocks and validate these through open-sourced simulation studies. A critical threshold for the adversary issuance rate is shown to exist, above which the tip pool becomes unstable, while for values below the orphanage decrease exponentially. We study the robustness of the protocol with an expiration time on tips, also called garbage collection, and modification of the parent references per block.
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Submitted 2 May, 2023;
originally announced May 2023.
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Report on the energy consumption of the IOTA 2.0 prototype network (GoShimmer 0.8.3) under different testing scenarios
Authors:
Louis Helmer,
Andreas Penzkofer
Abstract:
The high energy consumption of proof of work-based distributed ledgers has become an important environmental concern. Bitcoin, for example, consumes as much energy in a year as a developed country. Alternative consensus mechanisms, such as proof of stake, have been shown to use drastically less energy than proof of work-based DLTs. For example, the IOTA DLT, built upon a directed acyclic graph (DA…
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The high energy consumption of proof of work-based distributed ledgers has become an important environmental concern. Bitcoin, for example, consumes as much energy in a year as a developed country. Alternative consensus mechanisms, such as proof of stake, have been shown to use drastically less energy than proof of work-based DLTs. For example, the IOTA DLT, built upon a directed acyclic graph (DAG) architecture, uses an alternative consensus mechanism that requires significantly less energy than other DLTs. Because the (DLT) space is constantly and rapidly evolving, the question of how much energy DLTs actually consume demands to be continuously studied and answered. Previous research into the energy consumption of the IOTA network has shown that an optimization in the overall protocol correlates to an optimization in energy consumption. The planned IOTA 2.0 update, built upon the GoShimmer research prototype, promises to further optimize the protocol by removing the network's centralized Coordinator. This report presents the results of measuring the energy consumption of a private GoShimmer network while comparing these findings to previous research into the current mainnet, which is called Chrysalis. The main findings of this report are that the IOTA 2.0 research prototype shows both improvements and increase in the energy consumption metrics compared to the Chrysalis network. Additionally, this report defines a model to estimate the total annual energy consumption of an IOTA network. This model should be significant for future research as it enables a way to estimate the total cost of running the IOTA network as well as its carbon emissions. Moreover, having an annual power consumption metric allows for better objective comparisons to different DLTs.
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Submitted 25 October, 2022;
originally announced October 2022.
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Robustness of the Tangle 2.0 Consensus
Authors:
Bing-Yang Lin,
Daria Dziubałtowska,
Piotr Macek,
Andreas Penzkofer,
Sebastian Müller
Abstract:
In this paper, we investigate the performance of the Tangle 2.0 consensus protocol in a Byzantine environment. We use an agent-based simulation model that incorporates the main features of the Tangle 2.0 consensus protocol. Our experimental results demonstrate that the Tangle 2.0 protocol is robust to the bait-and-switch attack up to the theoretical upper bound of the adversary's 33% voting weight…
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In this paper, we investigate the performance of the Tangle 2.0 consensus protocol in a Byzantine environment. We use an agent-based simulation model that incorporates the main features of the Tangle 2.0 consensus protocol. Our experimental results demonstrate that the Tangle 2.0 protocol is robust to the bait-and-switch attack up to the theoretical upper bound of the adversary's 33% voting weight. We further show that the common coin mechanism in Tangle 2.0 is necessary for robustness against powerful adversaries. Moreover, the experimental results confirm that the protocol can achieve around 1s confirmation time in typical scenarios and that the confirmation times of non-conflicting transactions are not affected by the presence of conflicts.
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Submitted 24 August, 2022; v1 submitted 17 August, 2022;
originally announced August 2022.
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Tangle 2.0 Leaderless Nakamoto Consensus on the Heaviest DAG
Authors:
Sebastian Müller,
Andreas Penzkofer,
Nikita Polyanskii,
Jonas Theis,
William Sanders,
Hans Moog
Abstract:
We introduce the theoretical foundations of the Tangle 2.0, a probabilistic leaderless consensus protocol based on a directed acyclic graph (DAG) called the Tangle. The Tangle naturally succeeds the blockchain as its next evolutionary step as it offers features suited to establish more efficient and scalable distributed ledger solutions.
Consensus is no longer found in the longest chain but on t…
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We introduce the theoretical foundations of the Tangle 2.0, a probabilistic leaderless consensus protocol based on a directed acyclic graph (DAG) called the Tangle. The Tangle naturally succeeds the blockchain as its next evolutionary step as it offers features suited to establish more efficient and scalable distributed ledger solutions.
Consensus is no longer found in the longest chain but on the heaviest DAG, where PoW is replaced by a stake- or reputation-based weight function. The DAG structure and the underlying Reality-based UTXO Ledger allow parallel validation of transactions without the need for total ordering. Moreover, it enables the removal of the intermediary of miners and validators, allowing a pure two-step process that follows the \emph{propose-vote} paradigm at the node level and not at the validator level.
We propose a framework to analyse liveness and safety under different communication and adversary models. This allows providing impossibility results in some edge cases and in the asynchronous communication model. We provide formal proof of the security of the protocol assuming a common random coin.
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Submitted 12 October, 2022; v1 submitted 4 May, 2022;
originally announced May 2022.
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Reality-based UTXO Ledger
Authors:
Sebastian Müller,
Andreas Penzkofer,
Nikita Polyanskii,
Jonas Theis,
William Sanders,
Hans Moog
Abstract:
The Unspent Transaction Output (UTXO) model is commonly used in the field of Distributed Ledger Technology (DLT) to transfer value between participants. One of its advantages is that it allows parallel processing of transactions, as independent transactions can be added in any order. This property of order invariance and parallelisability has potential benefits in terms of scalability. However, si…
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The Unspent Transaction Output (UTXO) model is commonly used in the field of Distributed Ledger Technology (DLT) to transfer value between participants. One of its advantages is that it allows parallel processing of transactions, as independent transactions can be added in any order. This property of order invariance and parallelisability has potential benefits in terms of scalability. However, since the UTXO Ledger is an append-only data structure, this advantage is compromised through the presence of conflicting transactions. We propose an extended UTXO Ledger model that optimistically updates the ledger and keeps track of the dependencies of the possible conflicts. In the presence of a conflict resolution mechanism, we propose a method to reduce the extended ledger back to a consistent UTXO Ledger.
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Submitted 7 August, 2023; v1 submitted 3 May, 2022;
originally announced May 2022.
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Salt-based autopeering for DLT-networks
Authors:
Sebastian Müller,
Angelo Capossele,
Bartosz Kuśmierz,
Vivian Lin,
Hans Moog,
Andreas Penzkofer,
Olivia Saa,
William Sanders,
Wolfgang Welz
Abstract:
The security of any Distributed Ledger Technology (DLT) depends on the safety of the network layer. Much effort has been put into understanding the consensus layer of DLTs. However, many network layer designs seem ad-hoc and lack a careful analysis of the influence of the design decisions on the whole DLT system. We propose a salt-based automated neighbor selection protocol that shows the inherent…
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The security of any Distributed Ledger Technology (DLT) depends on the safety of the network layer. Much effort has been put into understanding the consensus layer of DLTs. However, many network layer designs seem ad-hoc and lack a careful analysis of the influence of the design decisions on the whole DLT system. We propose a salt-based automated neighbor selection protocol that shows the inherent tradeoffs of certain design decisions and allows a quantitative treatment of some network topology requirements. This example may serve as a design framework and facilitate future research. We provide a selection of results from simulations to highlight some tradeoffs in the design decisions.
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Submitted 3 November, 2021;
originally announced November 2021.
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Impact of delay classes on the data structure in IOTA
Authors:
Andreas Penzkofer,
Olivia Saa,
Daria Dziubałtowska
Abstract:
In distributed ledger technologies (DLTs) with a directed acyclic graph (DAG) data structure, a message-issuing node can decide where to append that message and, consequently, how to grow the DAG. This DAG data structure can typically be decomposed into two pools of messages: referenced messages and unreferenced messages (tips). The selection of the parent messages to which a node appends the mess…
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In distributed ledger technologies (DLTs) with a directed acyclic graph (DAG) data structure, a message-issuing node can decide where to append that message and, consequently, how to grow the DAG. This DAG data structure can typically be decomposed into two pools of messages: referenced messages and unreferenced messages (tips). The selection of the parent messages to which a node appends the messages it issues, depends on which messages it considers as tips. However, the exact time that a message enters the tip pool of a node depends on the delay of that message. In previous works, it was considered that messages have the same or similar delay; however, this generally may not be the case. We introduce the concept of classes of delays, where messages belonging to a certain class have a specific delay, and where these classes coexist in the DAG. We provide a general model that predicts the tip pool size for any finite number of different classes.
This categorisation and model is applied to the first iteration of the IOTA 2.0 protocol (a.k.a. Coordicide), where two distinct classes, namely value and data messages, coexist. We show that the tip pool size depends strongly on the dominating class that is present. Finally, we provide a methodology for controlling the tip pool size by dynamically adjusting the number of references a message creates.
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Submitted 12 October, 2021;
originally announced October 2021.
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On Fairness in Voting Consensus Protocols
Authors:
Sebastian Müller,
Andreas Penzkofer,
Darcy Camargo,
Olivia Saa
Abstract:
Voting algorithms have been widely used as consensus protocols in the realization of fault-tolerant systems. These algorithms are best suited for distributed systems of nodes with low computational power or heterogeneous networks, where different nodes may have different levels of reputation or weight. Our main contribution is the construction of a fair voting protocol in the sense that the influe…
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Voting algorithms have been widely used as consensus protocols in the realization of fault-tolerant systems. These algorithms are best suited for distributed systems of nodes with low computational power or heterogeneous networks, where different nodes may have different levels of reputation or weight. Our main contribution is the construction of a fair voting protocol in the sense that the influence of the eventual outcome of a given participant is linear in its weight. Specifically, the fairness property guarantees that any node can actively participate in the consensus finding even with low resources or weight. We investigate effects that may arise from weighted voting, such as loss of anonymity, centralization, scalability, and discuss their relevance to protocol design and implementation.
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Submitted 15 December, 2020;
originally announced December 2020.
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Fast Probabilistic Consensus with Weighted Votes
Authors:
Sebastian Müller,
Andreas Penzkofer,
Bartosz Kuśmierz,
Darcy Camargo,
William J. Buchanan
Abstract:
The fast probabilistic consensus (FPC) is a voting consensus protocol that is robust and efficient in Byzantine infrastructure. We propose an adaption of the FPC to a setting where the voting power is proportional to the nodes reputations. We model the reputation using a Zipf law and show using simulations that the performance of the protocol in Byzantine infrastructure increases with the Zipf exp…
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The fast probabilistic consensus (FPC) is a voting consensus protocol that is robust and efficient in Byzantine infrastructure. We propose an adaption of the FPC to a setting where the voting power is proportional to the nodes reputations. We model the reputation using a Zipf law and show using simulations that the performance of the protocol in Byzantine infrastructure increases with the Zipf exponent. Moreover, we propose several improvements of the FPC that decrease the failure rates significantly and allow the protocol to withstand adversaries with higher weight. We distinguish between cautious and berserk strategies of the adversaries and propose an efficient method to detect the more harmful berserk strategies. Our study refers at several points to a specific implementation of the IOTA protocol, but the principal results hold for general implementations of reputation models.
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Submitted 1 June, 2020;
originally announced June 2020.
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Parasite Chain Detection in the IOTA Protocol
Authors:
Andreas Penzkofer,
Bartosz Kusmierz,
Angelo Capossele,
William Sanders,
Olivia Saa
Abstract:
In recent years several distributed ledger technologies based on directed acyclic graphs (DAGs) have appeared on the market. Similar to blockchain technologies, DAG-based systems aim to build an immutable ledger and are faced with security concerns regarding the irreversibility of the ledger state. However, due to their more complex nature and recent popularity, the study of adversarial actions ha…
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In recent years several distributed ledger technologies based on directed acyclic graphs (DAGs) have appeared on the market. Similar to blockchain technologies, DAG-based systems aim to build an immutable ledger and are faced with security concerns regarding the irreversibility of the ledger state. However, due to their more complex nature and recent popularity, the study of adversarial actions has received little attention so far. In this paper we are concerned with a particular type of attack on the IOTA cryptocurrency, more specifically a Parasite Chain attack that attempts to revert the history stored in the DAG structure, also called the Tangle.
In order to improve the security of the Tangle, we present a detection mechanism for this type of attack. In this mechanism, we embrace the complexity of the DAG structure by sampling certain aspects of it, more particularly the distribution of the number of approvers. We initially describe models that predict the distribution that should be expected for a Tangle without any malicious actors. We then introduce metrics that compare this reference distribution with the measured distribution. Upon detection, measures can then be taken to render the attack unsuccessful. We show that due to a form of the Parasite Chain that is different from the main Tangle it is possible to detect certain types of malicious chains. We also show that although the attacker may change the structure of the Parasite Chain to avoid detection, this is done so at a significant cost since the attack is rendered less efficient.
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Submitted 28 April, 2020;
originally announced April 2020.
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Properties of the Tangle for Uniform Random and Random Walk Tip Selection
Authors:
Bartosz Kusmierz,
William Sanders,
Andreas Penzkofer,
Angelo Capossele,
Alon Gal
Abstract:
The growing number of applications for distributed ledger technologies is driving both industry and academia to solve the limitations of blockchain, particularly its scalability issues. Recent distributed ledger technologies have replaced the blockchain linear structure with a more flexible directed acyclic graph in an attempt to accommodate a higher throughput. Despite the fast-growing diffusion…
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The growing number of applications for distributed ledger technologies is driving both industry and academia to solve the limitations of blockchain, particularly its scalability issues. Recent distributed ledger technologies have replaced the blockchain linear structure with a more flexible directed acyclic graph in an attempt to accommodate a higher throughput. Despite the fast-growing diffusion of directed acyclic graph based distributed ledger technologies, researchers lack a basic understanding of their behavior. In this paper we analyze the Tangle, a directed acyclic graph that is used (with certain modifications) in various protocols such as IOTA, Byteball, Avalanche or SPECTRE. Our contribution is threefold. First, we run simulations in a continuous-time model to examine tip count stability and cumulative weight evolution while varying the rate of incoming transactions. In particular we confirm analytical predictions on the number of tips with uniform random tip selection strategy. Second, we show how different tip selection algorithms affect the growth of the Tangle. Moreover, we explain these differences by analyzing the spread of exit probabilities of random walks. Our findings confirm analytically derived predictions and provide novel insights on the different phases of growth of cumulative weight as well as on the average time difference for a transaction to receive its first approval when using distinct tip selection algorithms. Lastly, we analyze simulation overhead and performance as a function of Tangle size and compare results for different tip selection algorithms.
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Submitted 21 January, 2020;
originally announced January 2020.
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Robustness and efficiency of leaderless probabilistic consensus protocols within Byzantine infrastructures
Authors:
Angelo Capossele,
Sebastian Mueller,
Andreas Penzkofer
Abstract:
This paper investigates leaderless binary majority consensus protocols with low computational complexity in noisy Byzantine infrastructures. Using computer simulations, we show that explicit randomization of the consensus protocol can significantly increase the robustness towards faulty and malicious nodes. We identify the optimal amount of randomness for various Byzantine attack strategies on dif…
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This paper investigates leaderless binary majority consensus protocols with low computational complexity in noisy Byzantine infrastructures. Using computer simulations, we show that explicit randomization of the consensus protocol can significantly increase the robustness towards faulty and malicious nodes. We identify the optimal amount of randomness for various Byzantine attack strategies on different kinds of network topologies.
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Submitted 20 November, 2019;
originally announced November 2019.