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

Feng, Zhenni; Chen, Junchang; Liu, Tong

doi:10.1007/978-3-030-92635-9_32

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…It is reasonable that end devices only trade their data intermittently due to limited resources or mobility. In this paper, we further design a decentralized data trading platform which extends our previous work [26].…”

Section: Related Workmentioning

confidence: 86%

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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Section: Related Workmentioning

confidence: 86%

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

Self Cite

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show abstract

“…This calls for the development of an efficient data trading market. Auctions are one of the most popular mechanisms to motivate the IoT nodes to share their data and further earn money from the data [3][4][5][6]. Building on the characteristic of decentralization, it requires a symmetric peer-to-peer (P2P) auction paradigm for IoT data sharing.…”

Section: Introductionmentioning

confidence: 99%

“…The traditional electronic auction system, consisting of a centralized auctioneer and bidders, is an asymmetric structure [3,4,6]. The centralized asymmetric system faces trust issues, as a fully trustworthy auctioneer is difficult to realize [5].…”

Section: Introductionmentioning

confidence: 99%

Effective Consensus-Based Distributed Auction Scheme for Secure Data Sharing in Internet of Things

2022

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In a traditional electronic auction, the centralized auctioneer and decentralized bidders are in an asymmetric structure, where the auctioneer has more ability to decide the auction result. This asymmetric auction structure is not fair to the participants and not suitable for data auctions in the Internet of Things (IoT). The blockchain-based auction system, with participant equality and fairness, is typically symmetrical and particularly suitable for IoT data sharing. However, when applied to IoT data sharing in reality, it faces privacy and efficiency problems. In this context, how to guarantee privacy and break the inherent performance bottleneck of blockchain is still a major challenge. In this paper, a consensus-based distributed auction scheme is proposed for data sharing, which enforces privacy preservation and collusion resistance. A reverse auction-based decentralized data trading model is introduced to solve the trust problem without a centralized auctioneer, where bidders reach consensus on the auction result. Specifically, we devise a differentially private auction mechanism to incentivize data owners to participate in data sharing. An effective hybrid consensus algorithm is constructed among bidders to reach consensus on the auction result with improved security and efficiency. Theoretical analysis shows that the proposed scheme ensures the properties of privacy preservation, incentive compatibility and collusion resistance. Experimental results reveal that the proposed mechanism guarantees the data sharing efficiency and has certain scalability.

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Towards personalized privacy preference aware data trading: A contract theory based approach

Feng

Zhu

2023

Computer Networks

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

Cited by 3 publications

References 18 publications

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

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

Effective Consensus-Based Distributed Auction Scheme for Secure Data Sharing in Internet of Things

Towards personalized privacy preference aware data trading: A contract theory based approach

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