Uncovering Value of Correlated Data: Trading Data based on Iterative Combinatorial Auction

Feng, Zhenni; Chen, Junchang; Zhu, Yanmin

doi:10.1109/mass52906.2021.00042

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…Therefore, it is important to discover the correlation among the privacy sensitivity factor, the data quality of the gathered personal dataset, and the expected service quality of the data consumer in terms of setting parameters. Several studies, including [25,35], have tackled measuring and/or modeling the correlations, but still it is necessary to further studies on this topic.…”

Section: The Correlation Among δ α and Data Qualitymentioning

confidence: 99%

A Profit Maximization Model for Data Consumers with Data Providers’ Incentives in Personal Data Trading Market

Park,

Oh,

Choi

2023

Data

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This paper proposes a profit maximization model for a data consumer when it buys personal data from data providers (by obtaining consent) through data brokers and provides their new services to data providers (i.e., service consumers). To observe the behavioral models of data providers, the data consumer, and service consumers, this paper proposes the willingness-to-sell model of personal data of data providers (which is affected by data providers’ behavior related to explicit consent), the service quality model obtained by the collected personal data from the data consumer’s perspective, and the willingness-to-pay model of service consumers regarding provided new services from the data consumer. Particularly, this paper jointly considers the behavior of data providers and service users under a limited budget. With parameters inspired by real-world surveys on data providers, this paper shows various numerical results to check the feasibility of the proposed models.

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Section: The Correlation Among δ α and Data Qualitymentioning

confidence: 99%

A Profit Maximization Model for Data Consumers with Data Providers’ Incentives in Personal Data Trading Market

Park,

Oh,

Choi

2023

Data

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“…(2) Each data owner submits a bid including information of bidding price and active time (5) Each data owner whose bid is chosen uploads data to the distributed file system, which would return a location which describes where the data block is stored along with hash value of data (6) A data consumer offers payment to the data trading platform according to trading prices derived by the online data trading algorithm (7) The data trading platform checks payment and then sends the location of data along with hash value to the data consumer (8) The data consumer obtains the traded data block via the data location and check data using hash value (9) The data consumer sends a conformation of receiving data (10) Data owners finally receive payment For instance, there is a data request generated that requires road temperature and humidity data sometime. At the same time, three data owners with the required data submitted bids and their bid prices are 5, 7, and 10.…”

Section: Decentralized Data Trading Platformmentioning

confidence: 99%

“…Many recent studies [5][6][7][8][9] have paid attention to propose data trading approaches. Various kinds of datasets, such as raw data samples, range counts, and aggregate statistic results, are traded between data owners and data consumers.…”

Section: Introductionmentioning

confidence: 99%

A Near-Optimal Truthful Online Auction for Efficient Crowdsourced Data Trading with Dynamic Data Owners and Dynamic Data Requests

Feng

Zhang

Chen

et al. 2022

Wireless Communications and Mobile Computing

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Data is an extremely important asset in a modern scientific and commercial society. The life force behind powerful artificial intelligence (AI) or machine learning (ML) algorithms is data, especially lots of data, which makes data trading significantly essential to unlocking the power of AI or ML. Data owners who offer crowdsourced data and data consumers who request data blocks negotiate with each other to make an agreement on data assignment and trading prices via a data trading platform; consequently, both sides gain profit from the process of data trading. A great many existing studies have investigated various kinds of data sharing or trading as well as protecting data privacy or constructing a decentralized data trading platform due to mistrust issues. However, existing studies neglect an important characteristic, i.e., dynamics of both data owners and data requests in trading crowdsourced data collected by IoT devices. To this end, we first construct an auction-based model to formulate the data trading process and then propose a near-optimal online data trading algorithm that not only resolves the problem of matching dynamic data owners and randomly generated data requests but also determines the data trading price of each data block. The proposed algorithm achieves several good properties, such as a constant competitive ratio for near-optimal social efficiency, incentive compatibility, and individual rationality of participants, via rigorous theoretical analysis and extensive simulations. We further design a decentralized data trading platform in order to construct a practical data trading process incorporating the proposed data trading algorithm.

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An Online Truthful Auction for IoT Data Trading with Dynamic Data Owners

Feng

Chen

Liu

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Uncovering Value of Correlated Data: Trading Data based on Iterative Combinatorial Auction

Cited by 8 publications

References 22 publications

A Profit Maximization Model for Data Consumers with Data Providers’ Incentives in Personal Data Trading Market

A Profit Maximization Model for Data Consumers with Data Providers’ Incentives in Personal Data Trading Market

A Near-Optimal Truthful Online Auction for Efficient Crowdsourced Data Trading with Dynamic Data Owners and Dynamic Data Requests

An Online Truthful Auction for IoT Data Trading with Dynamic Data Owners

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