Open Access

ARTICLE

A Dynamic Multi-Attribute Resource Bidding Mechanism with Privacy Protection in Edge Computing

Shujuan Tian^1,2,3, Wenjian Ding^1,2,3, Gang Liu⁴, Yuxia Sun⁵, Saiqin Long⁵, Jiang Zhu^1,2,3,*

1 Xiangtan University, Xiangtan, 411105, China
2 Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan, 411105, China
3 Hunan International Scientific and Technological Cooperation Base of Intelligent Network, Xiangtan, 411105, China
4 Hunan University, Changsha, 410082, China
5 Jinan University, Guangzhou, 510632, China

* Corresponding Author: Jiang Zhu. Email:

Computers, Materials & Continua 2023, 75(1), 373-391. https://doi.org/10.32604/cmc.2023.034770

Received 27 July 2022; Accepted 09 November 2022; Issue published 06 February 2023

Abstract

In edge computing, a reasonable edge resource bidding mechanism can enable edge providers and users to obtain benefits in a relatively fair fashion. To maximize such benefits, this paper proposes a dynamic multi-attribute resource bidding mechanism (DMRBM). Most of the previous work mainly relies on a third-party agent to exchange information to gain optimal benefits. It is worth noting that when edge providers and users trade with third-party agents which are not entirely reliable and trustworthy, their sensitive information is prone to be leaked. Moreover, the privacy protection of edge providers and users must be considered in the dynamic pricing/transaction process, which is also very challenging. Therefore, this paper first adopts a privacy protection algorithm to prevent sensitive information from leakage. On the premise that the sensitive data of both edge providers and users are protected, the prices of providers fluctuate within a certain range. Then, users can choose appropriate edge providers by the price-performance ratio (PPR) standard and the reward of lower price (LPR) standard according to their demands. The two standards can be evolved by two evaluation functions. Furthermore, this paper employs an approximate computing method to get an approximate solution of DMRBM in polynomial time. Specifically, this paper models the bidding process as a non-cooperative game and obtains the approximate optimal solution based on two standards according to the game theory. Through the extensive experiments, this paper demonstrates that the DMRBM satisfies the individual rationality, budget balance, and privacy protection and it can also increase the task offloading rate and the system benefits.

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Keywords

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