In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely...In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.展开更多
This study addresses security and ethical challenges in LLM-based Multi-Agent Systems, as exemplified in a blockchain fraud detection case study. Leveraging blockchain’s secure architecture, the framework involves sp...This study addresses security and ethical challenges in LLM-based Multi-Agent Systems, as exemplified in a blockchain fraud detection case study. Leveraging blockchain’s secure architecture, the framework involves specialized LLM Agents—ContractMining, Investigative, Ethics, and PerformanceMonitor, coordinated by a ManagerAgent. Baseline LLM models achieved 30% accuracy with a threshold method and 94% accuracy with a random-forest method. The Claude 3.5-powered LLM system reached an accuracy of 92%. Ethical evaluations revealed biases, highlighting the need for fairness-focused refinements. Our approach aims to develop trustworthy and reliable networks of agents capable of functioning even in adversarial environments. To our knowledge, no existing systems employ ethical LLM agents specifically designed to detect fraud, making this a novel contribution. Future work will focus on refining ethical frameworks, scaling the system, and benchmarking it against traditional methods to establish a robust, adaptable, and ethically grounded solution for blockchain fraud detection.展开更多
针对多链式区块链采用主链最终共识机制,导致主链负载大,制约从链性能等问题,论文提出一种基于超图和MuSig2聚合签名的联盟链主从多链共识机制.首先根据超图理论,构建以横贯超图为主链,子超图为从链的联盟链主从多链架构;然后借鉴分治思...针对多链式区块链采用主链最终共识机制,导致主链负载大,制约从链性能等问题,论文提出一种基于超图和MuSig2聚合签名的联盟链主从多链共识机制.首先根据超图理论,构建以横贯超图为主链,子超图为从链的联盟链主从多链架构;然后借鉴分治思想,结合“背书-排序-验证”的共识方式,构建分层分类共识机制,通过分类处理交易降低主链负载压力;最后构建基于MuSig2聚合签名的联盟链多方背书签名方法,提升背书签名的验证效率.性能分析表明:基于MuSig2聚合签名的联盟链多方背书签名安全可靠,基于超图和MuSig2聚合签名的分层分类共识机制具有强一致性和线性时间复杂度.实验结果表明:基于MuSig2聚合签名的多方背书方法的总效率是椭圆曲线数字签名算法(Elliptic Curve Digital Signature Algorithm,ECDSA)的1.55倍,分层分类共识机制能够提升12.5%的共识效率.该机制具有较高性能,可满足企业多样化业务需求.展开更多
基金partially supported by the Science and Technology Development Fund,Macao SAR (0050/2020/A1)the National Natural Science Foundation of China (62103411, 72171230)。
文摘In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.
文摘This study addresses security and ethical challenges in LLM-based Multi-Agent Systems, as exemplified in a blockchain fraud detection case study. Leveraging blockchain’s secure architecture, the framework involves specialized LLM Agents—ContractMining, Investigative, Ethics, and PerformanceMonitor, coordinated by a ManagerAgent. Baseline LLM models achieved 30% accuracy with a threshold method and 94% accuracy with a random-forest method. The Claude 3.5-powered LLM system reached an accuracy of 92%. Ethical evaluations revealed biases, highlighting the need for fairness-focused refinements. Our approach aims to develop trustworthy and reliable networks of agents capable of functioning even in adversarial environments. To our knowledge, no existing systems employ ethical LLM agents specifically designed to detect fraud, making this a novel contribution. Future work will focus on refining ethical frameworks, scaling the system, and benchmarking it against traditional methods to establish a robust, adaptable, and ethically grounded solution for blockchain fraud detection.
文摘针对多链式区块链采用主链最终共识机制,导致主链负载大,制约从链性能等问题,论文提出一种基于超图和MuSig2聚合签名的联盟链主从多链共识机制.首先根据超图理论,构建以横贯超图为主链,子超图为从链的联盟链主从多链架构;然后借鉴分治思想,结合“背书-排序-验证”的共识方式,构建分层分类共识机制,通过分类处理交易降低主链负载压力;最后构建基于MuSig2聚合签名的联盟链多方背书签名方法,提升背书签名的验证效率.性能分析表明:基于MuSig2聚合签名的联盟链多方背书签名安全可靠,基于超图和MuSig2聚合签名的分层分类共识机制具有强一致性和线性时间复杂度.实验结果表明:基于MuSig2聚合签名的多方背书方法的总效率是椭圆曲线数字签名算法(Elliptic Curve Digital Signature Algorithm,ECDSA)的1.55倍,分层分类共识机制能够提升12.5%的共识效率.该机制具有较高性能,可满足企业多样化业务需求.
