Trading Data in Good Faith: Integrating Truthfulness and Privacy Preservation in Data Markets

Niu, Chaoyue; Zheng, Zhenzhe; Wu, Fan; Gao, Xiaofeng; Chen, Guihai

doi:10.1109/icde.2017.80

Cited by 35 publications

(13 citation statements)

References 6 publications

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“…Finally, the security and privacy category has also gained much attention in the literature. The topics covered in this category are related to privacy-preserving technology [89][90][91][92][93], property rights enforcement [94], and secure information models [95]. Data-as-a-Service Exploring the topic of Application Programming Interfaces (APIs) to enable data providers and buyers to use services of data marketplaces.…”

Section: The Technical Domainmentioning

confidence: 99%

Business Data Sharing through Data Marketplaces: A Systematic Literature Review

Abbas

Agahari

Ven

et al. 2021

JTAER

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Data marketplaces are expected to play a crucial role in tomorrow’s data economy, but such marketplaces are seldom commercially viable. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially not of neglected research topics that may advance such marketplaces toward commercialization. This study provides an overview of the state-of-the-art of data marketplace research. We employ a Systematic Literature Review (SLR) approach to examine 133 academic articles and structure our analysis using the Service-Technology-Organization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.

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Section: The Technical Domainmentioning

confidence: 99%

Business Data Sharing through Data Marketplaces: A Systematic Literature Review

Abbas

Agahari

Ven

et al. 2021

JTAER

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show abstract

“…However, regarding the data ecosystem and its marketplaces, there has been little discussion of the position or consideration of data in the businesses. There have been assessments of pricing models [36][37][38], trading model [28,29], and digital rights and privacy [39][40][41][42] in data exchange, but the purpose of data exchange and acquisition is the development of data-related services. Data trading is just one of the functions in the ecosystem, and without overall understanding of the value chain in the ecosystem, it is impossible to discuss the marketability of data.…”

Section: Relevant Studiesmentioning

confidence: 99%

Structural Characteristics of Stakeholder Relationships and Value Chain Network in Data Exchange Ecosystem

Hayashi

Ishimura²,

Ohsawa

2021

IEEE Access

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“…There exist a number of works about data acquisition [11][12][13][14] There also exist a lot of works about data trading, such as [15][16][17]. Specifically, Tian et al proposed a market mechanism considering the privacy leakage for trading IoT data in one-to-many trading scenarios [18].…”

Section: Related Workmentioning

confidence: 99%

Trading and Pricing Sensor Data in Competing Edge Servers with Double Auction Markets

Shi

Song

2021

Journal of Sensors

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With the development of the IoT (Internet of Things), sensors networks can bring a large amount of valuable data. In addition to be utilized in the local IoT applications, the data can also be traded in the connected edge servers. As an efficient resource allocation mechanism, the double auction has been widely used in the stock and futures markets and can be also applied in the data resource allocation in sensor networks. Currently, there usually exist multiple edge servers running double auctions competing with each other to attract data users (buyers) and producers (sellers). Therefore, the double auction market run on each edge server needs efficient mechanism to improve the allocation efficiency. Specifically, the pricing strategy of the double auction plays an important role on affecting traders’ profit, and thus, will affect the traders’ market choices and bidding strategies, which in turn affect the competition result of double auction markets. In addition, the traders’ trading strategies will also affect the market’s pricing strategy. Therefore, we need to analyze the double auction markets’ pricing strategy and traders’ trading strategies. Specifically, we use a deep reinforcement learning algorithm combined with mean field theory to solve this problem with a huge state and action space. For trading strategies, we use the Independent Parametrized Deep Q-Network (I-PDQN) algorithm combined with mean field theory to compute the Nash equilibrium strategies. We then compare it with the fictitious play (FP) algorithm. The experimental results show that the computation speed of I-PDQN algorithm is significantly faster than that of FP algorithm. For pricing strategies, the double auction markets will dynamically adjust the pricing strategy according to traders’ trading strategies. This is a sequential decision-making process involving multiple agents. Therefore, we model it as a Markov game. We adopt Multiagent Deep Deterministic Policy Gradient (MADDPG) algorithm to analyze the Nash equilibrium pricing strategies. The experimental results show that the MADDPG algorithm solves the problem faster than the FP algorithm.

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Trading Data in Good Faith: Integrating Truthfulness and Privacy Preservation in Data Markets

Cited by 35 publications

References 6 publications

Business Data Sharing through Data Marketplaces: A Systematic Literature Review

Business Data Sharing through Data Marketplaces: A Systematic Literature Review

Structural Characteristics of Stakeholder Relationships and Value Chain Network in Data Exchange Ecosystem

Trading and Pricing Sensor Data in Competing Edge Servers with Double Auction Markets

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