Payment Channel Networks(PCNs)are a promising alternative to improve the scalability of a blockchain network.A PCN employs off-chain micropayment channels that do not need a global block confirmation procedure,thereby...Payment Channel Networks(PCNs)are a promising alternative to improve the scalability of a blockchain network.A PCN employs off-chain micropayment channels that do not need a global block confirmation procedure,thereby sacrificing the ability to confirm transactions instantaneously.PCN uses a routing algorithm to identify a path between two users who do not have a direct channel between them to settle a transaction.The performance of most of the existing centralized path-finding algorithms does not scale with network size.The rapid growth of Bitcoin PCN necessitates considering distributed algorithms.However,the existing decentralized algorithms suffer from resource underutilization.We present a decentralized routing algorithm,Swift,focusing on fee optimization.The concept of a secret path is used to reduce the path length between a sender and a receiver to optimize the fees.Furthermore,we reduce a network structure into combinations of cycles to theoretically study fee optimization with changes in cloud size.The secret path also helps in edge load sharing,which results in an improvement of throughput.Swift routing achieves up to 21%and 63%in fee and throughput optimization,respectively.The results from the simulations follow the trends identified in the theoretical analysis.展开更多
Scalability has long been a major challenge of cryptocurrency systems,which is mainly caused by the delay in reaching consensus when processing transactions on-chain.As an effective mitigation approach,the payment cha...Scalability has long been a major challenge of cryptocurrency systems,which is mainly caused by the delay in reaching consensus when processing transactions on-chain.As an effective mitigation approach,the payment channel networks(PCNs)enable private channels among blockchain nodes to process transactions off-chain,relieving long-time waiting for the online transaction confirmation.The state-of-the-art studies of PCN focus on improving the efficiency and availability via optimizing routing,scheduling,and initial deposits,as well as preventing the system from security and privacy attacks.However,the behavioral decision dynamics of blockchain nodes under potential malicious attacks is largely neglected.To fill this gap,we employ the game theory to study the characteristics of channel interactions from both the micro and macro perspectives under the situation of channel depletion attacks.Our study is progressive,as we conduct the game-theoretic analysis of node behavioral characteristics from individuals to the whole population of PCN.Our analysis is complementary,since we utilize not only the classic game theory with the complete rationality assumption,but also the evolutionary game theory considering the limited rationality of players to portray the evolution of PCN.The results of numerous simulation experiments verify the effectiveness of our analysis.展开更多
Payment Channel Network(PCN)provides the off-chain settlement of transactions.It is one of the most promising solutions to solve the scalability issue of the blockchain.Many routing techniques in PCN have been propose...Payment Channel Network(PCN)provides the off-chain settlement of transactions.It is one of the most promising solutions to solve the scalability issue of the blockchain.Many routing techniques in PCN have been proposed.However,both incentive attack and privacy protection have not been considered in existing studies.In this paper,we present an auction-based system model for PCN routing using the Laplace differential privacy mechanism.We formulate the cost optimization problem to minimize the path cost under the constraints of the Hashed Time-Lock Contract(HTLC)tolerance and the channel capacity.We propose an approximation algorithm to find the top K shortest paths constrained by the HTLC tolerance and the channel capacity,i.e.,top K-restricted shortest paths.Besides,we design the probability comparison function to find the path with the largest probability of having the lowest path cost among the top K-restricted shortest paths as the final path.Moreover,we apply the binary search to calculate the transaction fee of each user.Through both theoretical analysis and extensive simulations,we demonstrate that the proposed routing mechanism can guarantee the truthfulness and individual rationality with the probabilities of 1/2 and 1/4,respectively.It can also ensure the differential privacy of the users.The experiments on the real-world datasets demonstrate that the privacy leakage of the proposed mechanism is 73.21%lower than that of the unified privacy protection mechanism with only 13.2%more path cost compared with the algorithm without privacy protection on average.展开更多
文摘Payment Channel Networks(PCNs)are a promising alternative to improve the scalability of a blockchain network.A PCN employs off-chain micropayment channels that do not need a global block confirmation procedure,thereby sacrificing the ability to confirm transactions instantaneously.PCN uses a routing algorithm to identify a path between two users who do not have a direct channel between them to settle a transaction.The performance of most of the existing centralized path-finding algorithms does not scale with network size.The rapid growth of Bitcoin PCN necessitates considering distributed algorithms.However,the existing decentralized algorithms suffer from resource underutilization.We present a decentralized routing algorithm,Swift,focusing on fee optimization.The concept of a secret path is used to reduce the path length between a sender and a receiver to optimize the fees.Furthermore,we reduce a network structure into combinations of cycles to theoretically study fee optimization with changes in cloud size.The secret path also helps in edge load sharing,which results in an improvement of throughput.Swift routing achieves up to 21%and 63%in fee and throughput optimization,respectively.The results from the simulations follow the trends identified in the theoretical analysis.
基金The work was partially supported by the National Key Research and Development Program of China under Grant No.2019YFB2102600the National Natural Science Foundation of China under Grant Nos.62122042,61971269 and 61832012.
文摘Scalability has long been a major challenge of cryptocurrency systems,which is mainly caused by the delay in reaching consensus when processing transactions on-chain.As an effective mitigation approach,the payment channel networks(PCNs)enable private channels among blockchain nodes to process transactions off-chain,relieving long-time waiting for the online transaction confirmation.The state-of-the-art studies of PCN focus on improving the efficiency and availability via optimizing routing,scheduling,and initial deposits,as well as preventing the system from security and privacy attacks.However,the behavioral decision dynamics of blockchain nodes under potential malicious attacks is largely neglected.To fill this gap,we employ the game theory to study the characteristics of channel interactions from both the micro and macro perspectives under the situation of channel depletion attacks.Our study is progressive,as we conduct the game-theoretic analysis of node behavioral characteristics from individuals to the whole population of PCN.Our analysis is complementary,since we utilize not only the classic game theory with the complete rationality assumption,but also the evolutionary game theory considering the limited rationality of players to portray the evolution of PCN.The results of numerous simulation experiments verify the effectiveness of our analysis.
基金supported by the National Natural Science Foundation of China under Grant Nos.61872193,61872191,and 62072254the Postgraduate Research and Practice Innovation Program of Jiangsu Province of China under Grant No.KYCX20_0762.
文摘Payment Channel Network(PCN)provides the off-chain settlement of transactions.It is one of the most promising solutions to solve the scalability issue of the blockchain.Many routing techniques in PCN have been proposed.However,both incentive attack and privacy protection have not been considered in existing studies.In this paper,we present an auction-based system model for PCN routing using the Laplace differential privacy mechanism.We formulate the cost optimization problem to minimize the path cost under the constraints of the Hashed Time-Lock Contract(HTLC)tolerance and the channel capacity.We propose an approximation algorithm to find the top K shortest paths constrained by the HTLC tolerance and the channel capacity,i.e.,top K-restricted shortest paths.Besides,we design the probability comparison function to find the path with the largest probability of having the lowest path cost among the top K-restricted shortest paths as the final path.Moreover,we apply the binary search to calculate the transaction fee of each user.Through both theoretical analysis and extensive simulations,we demonstrate that the proposed routing mechanism can guarantee the truthfulness and individual rationality with the probabilities of 1/2 and 1/4,respectively.It can also ensure the differential privacy of the users.The experiments on the real-world datasets demonstrate that the privacy leakage of the proposed mechanism is 73.21%lower than that of the unified privacy protection mechanism with only 13.2%more path cost compared with the algorithm without privacy protection on average.
