PureLottery: Fair and Bias-Resistant Leader Election with a Novel Single-Elimination Tournament Algorithm
- URL: http://arxiv.org/abs/2402.17459v1
- Date: Tue, 27 Feb 2024 12:30:17 GMT
- Title: PureLottery: Fair and Bias-Resistant Leader Election with a Novel Single-Elimination Tournament Algorithm
- Authors: Jonas Ballweg,
- Abstract summary: Leader Election (LE) is crucial in distributed systems and blockchain technology, ensuring one participant acts as the leader.
Traditional LE methods often depend on distributed random number generation (RNG), facing issues like vulnerability to manipulation, lack of fairness, and the need for complex procedures such as verifiable delay functions (VDFs) and publicly-verifiable secret sharing (PVSS)
This Bachelor's thesis presents a novel approach to randomized LE, leveraging a game-theoretic assumption that participants, aiming to be chosen as leaders, will naturally avoid actions that diminish their chances.
This perspective simplifies LE by eliminating the need for decentralized
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Leader Election (LE) is crucial in distributed systems and blockchain technology, ensuring one participant acts as the leader. Traditional LE methods often depend on distributed random number generation (RNG), facing issues like vulnerability to manipulation, lack of fairness, and the need for complex procedures such as verifiable delay functions (VDFs) and publicly-verifiable secret sharing (PVSS). This Bachelor's thesis presents a novel approach to randomized LE, leveraging a game-theoretic assumption that participants, aiming to be chosen as leaders, will naturally avoid actions that diminish their chances. This perspective simplifies LE by eliminating the need for decentralized RNG. Introducing PureLottery, inspired by single-elimination sports tournaments, this method offers a fair, bias-resistant, and efficient LE solution for blockchain environments. It operates on the principle of two participants competing in each match, rendering collusion efforts useless. PureLottery stands out for its low computational and communication complexity, suitable for smart contract implementation. It provides strong game-theoretic incentives for honesty and is robust against adversaries, ensuring no increase in election chances through dishonesty. The protocol guarantees that each honest player has at least a 1/n chance of winning, irrespective of adversary manipulation among the other n-1 participants. PureLottery can also address related problems like participant ranking, electing multiple leaders, and leader aversion, showcasing its versatility across various applications, including lotteries and blockchain protocols. An open-source implementation is made available for public use.
Related papers
- Tyche: Collateral-Free Coalition-Resistant Multiparty Lotteries with Arbitrary Payouts [23.27199615640474]
We propose Tyche, a family of protocols for performing efficient multiparty lotteries.
Our protocols are based on a commit-and-reveal approach, requiring only a collision-resistant hash function.
We show that our protocols are secure, fair, and some preserve the participants' privacy.
arXiv Detail & Related papers (2024-09-05T12:19:37Z) - Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities [69.34646544774161]
We formulate a new variant of multi-player multi-armed bandit (MAB) model, which captures arrival of requests to each arm and the policy of allocating requests to players.
The challenge is how to design a distributed learning algorithm such that players select arms according to the optimal arm pulling profile.
We design an iterative distributed algorithm, which guarantees that players can arrive at a consensus on the optimal arm pulling profile in only M rounds.
arXiv Detail & Related papers (2024-08-20T13:57:00Z) - Profitable Manipulations of Cryptographic Self-Selection are Statistically Detectable [4.704825771757308]
We study the detectability of profitable manipulations of the canonical cryptographic self-selection leader selection protocol introduced in [CM19].
We show that for any player with $alpha frac3-sqrt52 approx 0.38$ fraction of the total stake, every strictly profitable manipulation is statistically detectable.
arXiv Detail & Related papers (2024-07-24T02:34:01Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit [12.547006167704398]
We study a robust, i.e. in presence of malicious participants, multi-agent multi-armed bandit problem where multiple participants are distributed on a fully decentralized blockchain.
We are the first to incorporate advanced techniques from blockchains into a cooperative decision making framework to design optimal strategies for honest participants.
Notably, we are the first to prove the theoretical regret of the proposed algorithm and claim its optimality.
arXiv Detail & Related papers (2024-02-06T21:33:34Z) - Building Random, Fair, and Verifiable Games on Blockchain. Raffle smart
contract designs on Sui Network [10.410456199908587]
This paper aims to provide insights into designing a fair, verifiable, and efficient smart contract game on blockchain.
We explore efficient methods for implementing randomness on smart contracts, including DRAND committee-based decentralized random beacons and single private-key-based verifiable random functions (VRF)
Our findings provide valuable guidance for future researchers and developers in building random, fair, and verifiable games with smart contracts.
arXiv Detail & Related papers (2023-10-18T20:12:44Z) - A Game-theoretic Approach for Provably-Uniform Random Number Generation in Decentralized Networks [0.6216023343793144]
We provide a protocol for distributed generation of randomness.
It is trustless and generates unbiased random numbers.
It is also tamper-proof and no party can change the output or affect its distribution.
arXiv Detail & Related papers (2023-09-20T12:21:39Z) - Minimax-Optimal Multi-Agent RL in Zero-Sum Markov Games With a
Generative Model [50.38446482252857]
Two-player zero-sum Markov games are arguably the most basic setting in multi-agent reinforcement learning.
We develop a learning algorithm that learns an $varepsilon$-approximate Markov NE policy using $$ widetildeObigg.
We derive a refined regret bound for FTRL that makes explicit the role of variance-type quantities.
arXiv Detail & Related papers (2022-08-22T17:24:55Z) - Dual Lottery Ticket Hypothesis [71.95937879869334]
Lottery Ticket Hypothesis (LTH) provides a novel view to investigate sparse network training and maintain its capacity.
In this work, we regard the winning ticket from LTH as the subnetwork which is in trainable condition and its performance as our benchmark.
We propose a simple sparse network training strategy, Random Sparse Network Transformation (RST), to substantiate our DLTH.
arXiv Detail & Related papers (2022-03-08T18:06:26Z) - Secure Distributed Training at Scale [65.7538150168154]
Training in presence of peers requires specialized distributed training algorithms with Byzantine tolerance.
We propose a novel protocol for secure (Byzantine-tolerant) decentralized training that emphasizes communication efficiency.
arXiv Detail & Related papers (2021-06-21T17:00:42Z) - Multi-Stage Decentralized Matching Markets: Uncertain Preferences and
Strategic Behaviors [91.3755431537592]
This article develops a framework for learning optimal strategies in real-world matching markets.
We show that there exists a welfare-versus-fairness trade-off that is characterized by the uncertainty level of acceptance.
We prove that participants can be better off with multi-stage matching compared to single-stage matching.
arXiv Detail & Related papers (2021-02-13T19:25:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.