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
These days,data is regarded as a valuable asset in the era of the data economy,which demands a trading platform for buying and selling data.However,online data trading poses challenges in terms of security and fairnes...These days,data is regarded as a valuable asset in the era of the data economy,which demands a trading platform for buying and selling data.However,online data trading poses challenges in terms of security and fairness because the seller and the buyer may not fully trust each other.Therefore,in this paper,a blockchain-based secure and fair data trading system is proposed by taking advantage of the smart contract and matchmaking encryption.The proposed system enables bilateral authorization,where data trading between a seller and a buyer is accomplished only if their policies,required by each other,are satisfied simultaneously.This can be achieved by exploiting the security features of the matchmaking encryption.To guarantee non-repudiation and fairness between trading parties,the proposed system leverages a smart contract to ensure that the parties honestly carry out the data trading protocol.However,the smart contract in the proposed system does not include complex cryptographic operations for the efficiency of onchain processes.Instead,these operations are carried out by off-chain parties and their results are used as input for the on-chain procedure.The system also uses an arbitration protocol to resolve disputes based on the trading proof recorded on the blockchain.The performance of the protocol is evaluated in terms of off-chain computation overhead and on-chain gas consumption.The results of the experiments demonstrate that the proposed protocols can enable the implementation of a cost-effective data trading system.展开更多
The rapid development of social technology has replaced physical interaction in the trading market.The implication of this technology is to provide access to the right information at the right time.The drawback of the...The rapid development of social technology has replaced physical interaction in the trading market.The implication of this technology is to provide access to the right information at the right time.The drawback of these technologies is that the eavesdropper can remove the user from the network and can create proxy participants.In this paper,we discuss how a social network overcome and prevent these data trading issues.To maintain the security of data trading,we applied ABE technique based on DBDH to secure data trading network.Our proposedτ-access policy scheme provides the best solution for the betterment of data trading network in terms of security.Inτ-access policy scheme,the users can encrypt and decrypt Private Transactions Information(PTI)using our proposedτ-access policy.The security properties ofτ-access policy are data confidentiality,data integrity,authenticity,non-repudiation,and unforgeability.The efficiency of our scheme is 77.73%,which is more suitable for data trading markets and trading strategies.展开更多
Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambig...Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambiguous data rights,confusing pricing,and challenges in matching.Additionally,centralized IoT data trading platforms pose risks such as privacy leakage.To address these issues,we propose a profit-driven distributed trading mechanism for IoT data.First,a blockchain-based trading architecture for IoT data,leveraging the transparent and tamper-proof features of blockchain technology,is proposed to establish trust between data owners and data requesters.Second,an IoT data registration method that encompasses both rights confirmation and pricing is designed.The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data.For pricing,we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network.Finally,an IoT data matching method is designed based on the Stackelberg game.This establishes a Stackelberg game model involving multiple data owners and requesters,employing a hierarchical optimization method to determine the optimal purchase strategy.The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated.Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.展开更多
Large-quantity and high-quality data is critical to the success of machine learning in diverse applications.Faced with the dilemma of data silos where data is difficult to circulate,emerging data markets attempt to br...Large-quantity and high-quality data is critical to the success of machine learning in diverse applications.Faced with the dilemma of data silos where data is difficult to circulate,emerging data markets attempt to break the dilemma by facilitating data exchange on the Internet.Crowdsourcing,on the other hand,is one of the important methods to efficiently collect large amounts of data with high-value in data markets.In this paper,we investigate the joint problem of efficient data acquisition and fair budget distribution across the crowdsourcing and data markets.We propose a new metric of data value as the uncertainty reduction of a Bayesian machine learning model by integrating the data into model training.Guided by this data value metric,we design a mechanism called Shapley Value Mechanism with Individual Rationality(SV-IR),in which we design a greedy algorithm with a constant approximation ratio to greedily select the most cost-efficient data brokers,and a fair compensation determination rule based on the Shapley value,respecting the individual rationality constraints.We further propose a fair reward distribution method for the data holders with various effort levels under the charge of a data broker.We demonstrate the fairness of the compensation determination rule and reward distribution rule by evaluating our mechanisms on two real-world datasets.The evaluation results also show that the selection algorithm in SV-IR could approach the optimal solution,and outperforms state-of-the-art methods.展开更多
With the development of Big Data and the Internet of Things(IoT),the data value is more significant in both academia and industry.Trading can achieve maximal data value and prepare data for smart city services.Due to ...With the development of Big Data and the Internet of Things(IoT),the data value is more significant in both academia and industry.Trading can achieve maximal data value and prepare data for smart city services.Due to data's unique characteristics,such as dispersion,heterogeneity and distributed storage,an unbiased platform is necessary for the data trading market with rational trading entities.Meanwhile,there are multiple buyers and sellers in a practical data trading market,and this makes it challenging to maximize social welfare.To solve these problems,this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism(SOMTM),which integrates three entities,a trading process and an algorithm named Many-to-Many Trading Algorithm(MMTA).Based on the market scale,market dominated-side and market fixed-side,simulations verify the convergency,economic properties and efficiency of SOMTM.展开更多
China＇s foreign trade operation situation According to the statistics collected by Customs, in January-October 2017, China＇s total import and export value reached RMB 22.52 trillion, with an increase of 15.9% year o...China＇s foreign trade operation situation According to the statistics collected by Customs, in January-October 2017, China＇s total import and export value reached RMB 22.52 trillion, with an increase of 15.9% year on year.展开更多
With the growth of requirements for data sharing,a novel business model of digital assets trading has emerged that allows data owners to sell their data for monetary gain.In the distributed ledger of blockchain,howeve...With the growth of requirements for data sharing,a novel business model of digital assets trading has emerged that allows data owners to sell their data for monetary gain.In the distributed ledger of blockchain,however,the privacy of stakeholder's identity and the confidentiality of data content are threatened.Therefore,we proposed a blockchainenabled privacy-preserving and access control scheme to address the above problems.First,the multi-channel mechanism is introduced to provide the privacy protection of distributed ledger inside the channel and achieve coarse-grained access control to digital assets.Then,we use multi-authority attribute-based encryption(MAABE)algorithm to build a fine-grained access control model for data trading in a single channel and describe its instantiation in detail.Security analysis shows that the scheme has IND-CPA secure and can provide privacy protection and collusion resistance.Compared with other schemes,our solution has better performance in privacy protection and access control.The evaluation results demonstrate its effectiveness and practicability.展开更多
China’s foreign trade in the first ten months of 2011 According to statistics of the Customs, China’s exports and imports in the first ten months of the year reached $2.97538 trillion, up 24.3% over the same period ...China’s foreign trade in the first ten months of 2011 According to statistics of the Customs, China’s exports and imports in the first ten months of the year reached $2.97538 trillion, up 24.3% over the same period last year, 12 percentage展开更多
With the increasing global mobile data traffic and daily user engagement,technologies,such as mobile crowdsensing,benefit hugely from the constant data flows from smartphone and IoT owners.However,the device users,as ...With the increasing global mobile data traffic and daily user engagement,technologies,such as mobile crowdsensing,benefit hugely from the constant data flows from smartphone and IoT owners.However,the device users,as data owners,urgently require a secure and fair marketplace to negotiate with the data consumers.In this paper,we introduce a novel federated data acquisition market that consists of a group of local data aggregators(LDAs);a number of data owners;and,one data union to coordinate the data trade with the data consumers.Data consumers offer each data owner an individual price to stimulate participation.The mobile data owners naturally cooperate to gossip about individual prices with each other,which also leads to price fluctuation.It is challenging to analyse the interactions among the data owners and the data consumers using traditional game theory due to the complex price dynamics in a large-scale heterogeneous data acquisition scenario.Hence,we propose a data pricing strategy based on mean-field game(MFG)theory to model the data owners’cost considering the price dynamics.We then investigate the interactions among the LDAs by using the distribution of price,namely the mean-field term.A numerical method is used to solve the proposed pricing strategy.The evaluations demonstrate that the proposed pricing strategy efficiently allows the data owners from multiple LDAs to reach an equilibrium on data quantity to sell regarding the current individual price scheme.The result further demonstrates that the influential LDAs determine the final price distribution.Last but not least,it shows that cooperation among mobile data owners leads to optimal social welfare even with the additional cost of information exchange.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.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2022R1I1A3063257)supported by Electronics and Telecommunications Research Institute(ETRI)grant funded by the Korean Government[22ZR1300,Research on Intelligent Cyber Security and Trust Infra].
文摘These days,data is regarded as a valuable asset in the era of the data economy,which demands a trading platform for buying and selling data.However,online data trading poses challenges in terms of security and fairness because the seller and the buyer may not fully trust each other.Therefore,in this paper,a blockchain-based secure and fair data trading system is proposed by taking advantage of the smart contract and matchmaking encryption.The proposed system enables bilateral authorization,where data trading between a seller and a buyer is accomplished only if their policies,required by each other,are satisfied simultaneously.This can be achieved by exploiting the security features of the matchmaking encryption.To guarantee non-repudiation and fairness between trading parties,the proposed system leverages a smart contract to ensure that the parties honestly carry out the data trading protocol.However,the smart contract in the proposed system does not include complex cryptographic operations for the efficiency of onchain processes.Instead,these operations are carried out by off-chain parties and their results are used as input for the on-chain procedure.The system also uses an arbitration protocol to resolve disputes based on the trading proof recorded on the blockchain.The performance of the protocol is evaluated in terms of off-chain computation overhead and on-chain gas consumption.The results of the experiments demonstrate that the proposed protocols can enable the implementation of a cost-effective data trading system.
文摘The rapid development of social technology has replaced physical interaction in the trading market.The implication of this technology is to provide access to the right information at the right time.The drawback of these technologies is that the eavesdropper can remove the user from the network and can create proxy participants.In this paper,we discuss how a social network overcome and prevent these data trading issues.To maintain the security of data trading,we applied ABE technique based on DBDH to secure data trading network.Our proposedτ-access policy scheme provides the best solution for the betterment of data trading network in terms of security.Inτ-access policy scheme,the users can encrypt and decrypt Private Transactions Information(PTI)using our proposedτ-access policy.The security properties ofτ-access policy are data confidentiality,data integrity,authenticity,non-repudiation,and unforgeability.The efficiency of our scheme is 77.73%,which is more suitable for data trading markets and trading strategies.
基金supported by the National Key Research and Development Program of China(No.2022YFF0610003)the BUPT Excellent Ph.D.Students Foundation(No.CX2022218)the Fund of Central University Basic Research Projects(No.2023ZCTH11).
文摘Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambiguous data rights,confusing pricing,and challenges in matching.Additionally,centralized IoT data trading platforms pose risks such as privacy leakage.To address these issues,we propose a profit-driven distributed trading mechanism for IoT data.First,a blockchain-based trading architecture for IoT data,leveraging the transparent and tamper-proof features of blockchain technology,is proposed to establish trust between data owners and data requesters.Second,an IoT data registration method that encompasses both rights confirmation and pricing is designed.The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data.For pricing,we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network.Finally,an IoT data matching method is designed based on the Stackelberg game.This establishes a Stackelberg game model involving multiple data owners and requesters,employing a hierarchical optimization method to determine the optimal purchase strategy.The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated.Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.
基金supported in part by the National Key Research and Development Program of China under Grant No.2020YFB1707900the National Natural Science Foundation of China under Grant Nos.U2268204,62322206,62132018,62025204,62272307,and 62372296.
文摘Large-quantity and high-quality data is critical to the success of machine learning in diverse applications.Faced with the dilemma of data silos where data is difficult to circulate,emerging data markets attempt to break the dilemma by facilitating data exchange on the Internet.Crowdsourcing,on the other hand,is one of the important methods to efficiently collect large amounts of data with high-value in data markets.In this paper,we investigate the joint problem of efficient data acquisition and fair budget distribution across the crowdsourcing and data markets.We propose a new metric of data value as the uncertainty reduction of a Bayesian machine learning model by integrating the data into model training.Guided by this data value metric,we design a mechanism called Shapley Value Mechanism with Individual Rationality(SV-IR),in which we design a greedy algorithm with a constant approximation ratio to greedily select the most cost-efficient data brokers,and a fair compensation determination rule based on the Shapley value,respecting the individual rationality constraints.We further propose a fair reward distribution method for the data holders with various effort levels under the charge of a data broker.We demonstrate the fairness of the compensation determination rule and reward distribution rule by evaluating our mechanisms on two real-world datasets.The evaluation results also show that the selection algorithm in SV-IR could approach the optimal solution,and outperforms state-of-the-art methods.
文摘With the development of Big Data and the Internet of Things(IoT),the data value is more significant in both academia and industry.Trading can achieve maximal data value and prepare data for smart city services.Due to data's unique characteristics,such as dispersion,heterogeneity and distributed storage,an unbiased platform is necessary for the data trading market with rational trading entities.Meanwhile,there are multiple buyers and sellers in a practical data trading market,and this makes it challenging to maximize social welfare.To solve these problems,this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism(SOMTM),which integrates three entities,a trading process and an algorithm named Many-to-Many Trading Algorithm(MMTA).Based on the market scale,market dominated-side and market fixed-side,simulations verify the convergency,economic properties and efficiency of SOMTM.
文摘China＇s foreign trade operation situation According to the statistics collected by Customs, in January-October 2017, China＇s total import and export value reached RMB 22.52 trillion, with an increase of 15.9% year on year.
基金supported by National Key Research and Development Plan in China(Grant No.2020YFB1005500)Beijing Natural Science Foundation(Grant No.M21034)BUPT Excellent Ph.D Students Foundation(Grant No.CX2023218)。
文摘With the growth of requirements for data sharing,a novel business model of digital assets trading has emerged that allows data owners to sell their data for monetary gain.In the distributed ledger of blockchain,however,the privacy of stakeholder's identity and the confidentiality of data content are threatened.Therefore,we proposed a blockchainenabled privacy-preserving and access control scheme to address the above problems.First,the multi-channel mechanism is introduced to provide the privacy protection of distributed ledger inside the channel and achieve coarse-grained access control to digital assets.Then,we use multi-authority attribute-based encryption(MAABE)algorithm to build a fine-grained access control model for data trading in a single channel and describe its instantiation in detail.Security analysis shows that the scheme has IND-CPA secure and can provide privacy protection and collusion resistance.Compared with other schemes,our solution has better performance in privacy protection and access control.The evaluation results demonstrate its effectiveness and practicability.
文摘China’s foreign trade in the first ten months of 2011 According to statistics of the Customs, China’s exports and imports in the first ten months of the year reached $2.97538 trillion, up 24.3% over the same period last year, 12 percentage
基金supported within the project TRACE-V2Xfunding from the European Union’s HORIZON-MSCA-2022-SE-01-01 under grant agreement(101131204).
文摘With the increasing global mobile data traffic and daily user engagement,technologies,such as mobile crowdsensing,benefit hugely from the constant data flows from smartphone and IoT owners.However,the device users,as data owners,urgently require a secure and fair marketplace to negotiate with the data consumers.In this paper,we introduce a novel federated data acquisition market that consists of a group of local data aggregators(LDAs);a number of data owners;and,one data union to coordinate the data trade with the data consumers.Data consumers offer each data owner an individual price to stimulate participation.The mobile data owners naturally cooperate to gossip about individual prices with each other,which also leads to price fluctuation.It is challenging to analyse the interactions among the data owners and the data consumers using traditional game theory due to the complex price dynamics in a large-scale heterogeneous data acquisition scenario.Hence,we propose a data pricing strategy based on mean-field game(MFG)theory to model the data owners’cost considering the price dynamics.We then investigate the interactions among the LDAs by using the distribution of price,namely the mean-field term.A numerical method is used to solve the proposed pricing strategy.The evaluations demonstrate that the proposed pricing strategy efficiently allows the data owners from multiple LDAs to reach an equilibrium on data quantity to sell regarding the current individual price scheme.The result further demonstrates that the influential LDAs determine the final price distribution.Last but not least,it shows that cooperation among mobile data owners leads to optimal social welfare even with the additional cost of information exchange.
