Mathematical Research for Blockchain Economy

Name: Mathematical Research for Blockchain Economy | 4th International Conference MARBLE 2023, London, United Kingdom
Brand: Springer
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4th International Conference MARBLE 2023, London, United Kingdom

Panos Pardalos Ilias Kotsireas William J. Knottenbelt Stefanos Leonardos(Herausgeber*in)

Springer (Verlag)

1. Auflage

Erschienen am 14. Dezember 2023

VII, 187 Seiten

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Panos Pardalos is an emeritus distinguished professor of Industrial and Systems Engineering at the University of Florida, USA. He also holds the Paul and Heidi Brown Preeminent Professor title in the same department. Additionally, he is the director of the Center for Applied Optimization and is an affiliated faculty member of the Computer and Information Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program. Professor Pardalos is a world-leading expert in global and combinatorial optimization. He is also a member of several Academies of Sciences and holds honorary Ph.D. degrees and affiliations. He is a founding editor of Optimization Letters, Energy Systems, and co-founder of the International Journal of Global Optimization, and Computational Management Science. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.

Ilias S. Kotsireas is a full professor of Computer Science at Wilfrid Laurier University in Waterloo, Ontario, Canada. He has the founder and director of the CARGO Lab for more than 15 years. He has published over 150 research works in computational algebra, metaheuristics, high-performance computing, dynamical systems, and combinatorial design theory. He is on seven international journals' editorial boards, including being a managing editor of a Springer journal and an editor-in-chief for another Springer journal and a Birkhauser book series. He has organized numerous international conferences in Europe, North America, and Asia, often leading as a program committee chair or general chair.

William Knottenbelt is a professor of Applied Quantitative Analysis at the Department of Computing, Imperial College, London, UK. His broad area of research interest is the application of mathematical modelling techniques to real-life systems. Specific areas of interest include but are not limited to modelling and optimisation in parallel queuing systems, modelling of storage systems, stochastic modelling of sport, stochastic modelling of healthcare systems, resource allocation and control in cloud-computing environments, numerical solution of Markov models and specification techniques for SLA specification, compliance prediction and monitoring.

Stefanos Leonardos is a lecturer in Machine Learning at the Department of Informatics, King's College London, UK. His research interests span the areas of game theory and its applications in blockchain-enabled, digital economic systems, artificial intelligence, machine learning, and in strategic interactions that dynamically evolve over time. He has won numerous best paper awards for his research on the design of decentralized economies and on the analysis of exploration-exploitation algorithms in multi-agent systems.

Inhalt

Intro
Preface
Contents
Deep Reinforcement Learning-Based Rebalancing Policies for Profit Maximization of Relay Nodes in Payment Channel Networks
1 Introduction
2 Background
2.1 Payment Channel Networks and the Need for Rebalancing
2.2 The Submarine Swap Rebalancing Mechanism
3 Problem Formulation
3.1 System Evolution
3.2 Writing the Problem as a Markov Decision Process
4 Heuristic and Reinforcement Learning-Based Policies
4.1 Heuristic Policies
4.2 Deep Reinforcement Learning Algorithm Design
5 Evaluation
6 Related Work
7 Conclusion
A Causes of Channel Depletion
B The Submarine Swap Protocol
C An Equivalent Objective
D Deep Reinforcement Learning Algorithm Design Details
D.1 Helping a Swap-In Succeed
D.2 Design Choices
D.3 Practical Applicability
E Hyperparameters and Rewards
F Additional Experimental Results
F.1 The RebEL Policy Under Even Demand
F.2 The Role of the Initial Conditions
References
Game-Theoretic Randomness for Proof-of-Stake
1 Introduction
2 Preliminaries
2.1 Games and Equilibria
2.2 Publicly-Verifiable Secret Sharing
2.3 Verifiable Delay Functions
3 Random Integer Generation Game (RIG)
3.1 Overview of RIG
3.2 Analysis of Alliance-Resistant Nash Equilibria
3.3 Dense RIG Bimatrix Game
4 Designing a Random Beacon Based on RIG
4.1 Commitment Scheme and VDF Approach
4.2 PVSS Approach
4.3 Further Details of the Approach
4.4 Assumptions and Limits to Applicability
5 RIG in Proof of Stake Protocols
5.1 RIG in Ouroboros Praos
5.2 RIG in Algorand
6 Conclusion
References
Incentive Schemes for Rollup Validators
1 Introduction
2 Model
2.1 Extension to n+1 Validators
2.2 Silent Validators
3 Protocol Level Incentives
4 Conclusions and Future Work
References
Characterizing Common Quarterly Behaviors in DeFi Lending Protocols
1 Introduction
2 Methods
2.1 Data Sources
2.2 Transaction-Level Data
2.3 Address-Level Summaries
2.4 Computation of Clusters
3 Results
3.1 Interpretations of Clusters
3.2 Insights Derived From Clusters
4 Related Work
5 Discussion and Future Work
References
Blockchain Transaction Censorship: (In)secure and (In)efficient?
1 Introduction
2 Background
2.1 Blockchain and Smart Contracts
2.2 Centralized Transaction Propagation Services
2.3 ZKP Mixers
2.4 Blockchain Regulation and Censorship
3 System Model
3.1 System Components
3.2 Blockchain Censoring
3.3 Threat Model
4 Censorship During Transaction Validation
4.1 Miners' Censorship on Tainted Transactions
4.2 DoS Censoring Miners Through Crafting Tainted Transactions
4.3 Attack Cost
5 Censorship During Transaction Propagation
5.1 FaaS Workflow
5.2 FaaS Censorship Mechanism
5.3 DoS Censoring FaaS Searchers and Builders
6 Censorship During Transaction Generation
6.1 Non-transparent Frontend-Level Censorship
6.2 Investigating DeFi Platforms' Censorship
6.3 Tainting Innocent Addresses
6.4 Bypassing Frontend-Level Censorship
7 Related Work
8 Conclusion
References
An Automated Market Maker Minimizing Loss-Versus-Rebalancing
1 Introduction
1.1 Our Contribution
1.2 Organization of the Paper
2 Related Work
3 Preliminaries
3.1 Constant Function Market Makers
3.2 Loss-Versus-Rebalancing
3.3 Auctions
4 Diamond
4.1 Model Assumptions
4.2 Core Protocol
4.3 Per-block Conversion Versus Future Contracts
4.4 Periodic Conversion Auction
5 Diamond Properties
6 Implementation
6.1 Core Protocol
6.2 Conversion Protocols
7 Experimental Analysis
8 Conclusion
A Proofs
References
Profit Lag and Alternate Network Mining
1 Introduction
1.1 Nakamoto Consensus
1.2 Mining Process
1.3 Selfish Mining
1.4 Smart Mining
1.5 Intermittent Selfish Mining
1.6 Alternate Network Mining
1.7 Organization of This Article
2 Modelization
2.1 Mining and Difficulty Adjustment Formula
2.2 Notations
2.3 Profitability of a Mining Strategy
2.4 Attack Cycles
2.5 Performant Strategy and Profit Lag
3 Selfish Mining Revisited
3.1 Previous State-Machine Approach Revisited
3.2 Profit Lag
4 Intermittent Selfish Mining Strategy
4.1 Profit Lag
5 Alternate Network Mining Strategy
5.1 Profit Lag
6 Conclusion
References
Oracle Counterpoint: Relationships Between On-Chain and Off-Chain Market Data
1 Introduction
2 Methods
2.1 Fundamental Economic Features from On-Chain Markets
2.2 Data-Driven Feature Analysis
2.3 Modeling Off-Chain Prices
3 Results
3.1 Feature Analysis
3.2 Recovering Off-Chain Prices from On-Chain Data
3.3 Performance of Price Recovery
4 Discussion
A More Details on Dataset Features
A.1.1 Economic Features
B Further Information on Ethereum Analysis
B.1.1 Performance of Price Recovery
C Analysis of Celo PoS Data
References
Exploring Decentralized Governance: A Framework Applied to Compound Finance
1 Introduction
1.1 Motivation
1.2 Contribution Summary
2 Compound
2.1 Governance
3 Relevant Work
3.1 Literature Discussion
4 Methodology and Data
4.1 Data
4.2 Methodology
4.3 Top 100 Leaderboard
4.4 Proposals
5 Discussion
6 Conclusions
Appendix A Proof for Gini and Nakamoto Coefficients When n = 2 and u1 = u2
References
A Mathematical Approach on the Use of Integer Partitions for Smurfing in Cryptocurrencies
1 Introduction
2 Related Work
3 Using Integer Partitions to Create Patterns for Smurfing
3.1 Problem Formulation
3.2 Possible Implications of the Conceptualization of Smurfing via Integer Partitions
3.3 Examples for Integer Partitions and Smurfing
4 Conclusion and Future Work
References
Bigger Than We Thought: The Upbit Hack Gang
1 Introduction
2 Rough ML Network Construction
2.1 Crawling Tool and Event
2.2 Account and Transaction Data Crawling
2.3 Network Construction
3 ML Network Refinement
3.1 Design an ML Suspiciousness Indicator
3.2 Calculate ML Risks of Accounts
4 Results and Analysis
5 Conclusion
References
Staking Pools on Blockchains

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