The blockchain technology has been applied to wide areas.However,the open and transparent properties of the blockchains pose serious challenges to users’privacy.Among all the schemes for the privacy protection,the ze...The blockchain technology has been applied to wide areas.However,the open and transparent properties of the blockchains pose serious challenges to users’privacy.Among all the schemes for the privacy protection,the zero-knowledge proof algorithm conceals most of the private information in a transaction,while participants of the blockchain can validate this transaction without the private information.However,current schemes are only aimed at blockchains with the UTXO model,and only one type of assets circulates on these blockchains.Based on the zero-knowledge proof algorithm,this paper proposes a privacy protection scheme for blockchains that use the account and multi-asset model.We design the transaction structure,anonymous addresses and anonymous asset metadata,and also propose the methods of the asset transfer and double-spending detection.The zk-SNARKs algorithm is used to generate and to verify the zero-knowledge proof.And finally,we conduct the experiments to evaluate our scheme.展开更多
Artificial intelligence(AI)is the core technology of technological revolution and industrial transformation.As one of the new intelligent needs in the AI 2.0 era,financial intelligence has elicited much attention from...Artificial intelligence(AI)is the core technology of technological revolution and industrial transformation.As one of the new intelligent needs in the AI 2.0 era,financial intelligence has elicited much attention from the academia and industry.In our current dynamic capital market,financial intelligence demonstrates a fast and accurate machine learning capability to handle complex data and has gradually acquired the potential to become a'financial brain.'In this paper,we survey existing studies on financial intelligence.First,we describe the concept of financial intelligence and elaborate on its position in the financial technology field.Second,we introduce the development of financial intelligence and review state-of-the-art techniques in wealth management,risk management,financial security,financial consulting,and blockchain.Finally,we propose a research framework called FinBrain and summarize four open issues,namely,explainable financial agents and causality,perception and prediction under uncertainty,risk-sensitive and robust decision-making,and multi-agent game and mechanism design.We believe that these research directions can lay the foundation for the development of AI 2.0 in the finance field.展开更多
Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in parallel.Among them,local-based algorithms,including Local S...Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in parallel.Among them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and privacypreserving.Nevertheless,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence rate.In this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous data.Without extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence rate.Moreover,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite diverse.Benefiting from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical datasets.We conduct experiments on three machine learning tasks.The experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.展开更多
基金supported by National Natural Science Foundation of China(61672499,61772502)Key Special Project of Beijing Municipal Science&Technology Commission(Z181100003218018)+1 种基金Natural Science Foundation of Inner Mongolia,Open Foundation of State key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications,SKLNST-2016-2-09)SV-ICT Blockchain&DAPP Joint Lab
文摘The blockchain technology has been applied to wide areas.However,the open and transparent properties of the blockchains pose serious challenges to users’privacy.Among all the schemes for the privacy protection,the zero-knowledge proof algorithm conceals most of the private information in a transaction,while participants of the blockchain can validate this transaction without the private information.However,current schemes are only aimed at blockchains with the UTXO model,and only one type of assets circulates on these blockchains.Based on the zero-knowledge proof algorithm,this paper proposes a privacy protection scheme for blockchains that use the account and multi-asset model.We design the transaction structure,anonymous addresses and anonymous asset metadata,and also propose the methods of the asset transfer and double-spending detection.The zk-SNARKs algorithm is used to generate and to verify the zero-knowledge proof.And finally,we conduct the experiments to evaluate our scheme.
基金Project supported by the National Natural Science Foundation of China(No.U1509221)the National Key Technology R&D Program of China(No.2015BAH07F01)the Zhejiang Provincial Key R&D Program,China(No.2017C03044)
文摘Artificial intelligence(AI)is the core technology of technological revolution and industrial transformation.As one of the new intelligent needs in the AI 2.0 era,financial intelligence has elicited much attention from the academia and industry.In our current dynamic capital market,financial intelligence demonstrates a fast and accurate machine learning capability to handle complex data and has gradually acquired the potential to become a'financial brain.'In this paper,we survey existing studies on financial intelligence.First,we describe the concept of financial intelligence and elaborate on its position in the financial technology field.Second,we introduce the development of financial intelligence and review state-of-the-art techniques in wealth management,risk management,financial security,financial consulting,and blockchain.Finally,we propose a research framework called FinBrain and summarize four open issues,namely,explainable financial agents and causality,perception and prediction under uncertainty,risk-sensitive and robust decision-making,and multi-agent game and mechanism design.We believe that these research directions can lay the foundation for the development of AI 2.0 in the finance field.
基金This research was partially supported by grants from the National Key Research and Development Program of China(No.2018YFC0832101)the National Natural Science Foundation of China(Grant Nos.U20A20229 and 61922073).
文摘Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in parallel.Among them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and privacypreserving.Nevertheless,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence rate.In this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous data.Without extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence rate.Moreover,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite diverse.Benefiting from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical datasets.We conduct experiments on three machine learning tasks.The experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.
