Privacy-Preserving and Truthful Auction for Task Assignment in Outsourced Cloud Environments

EasyChair Preprint no. 9387

Abstract

Due to high fairness and allocation efficiency, the task assignment problem of mobile applications via auctions has become a promising approach to motivate bidders to provide their mobile device resources effectively. However, most of existing works focus on the auction mechanism under the plaintexts, and ignore the problems caused by information leakage. In this paper, we study the problem of the privacy-preserving auction for task assignment in outsourced cloud environments without leaking any private information to anyone. Specifically, we use Yao's garbled circuits and homomorphic encryption system as underlying tools. Along with several elaborately designed secure arithmetic subroutines, we propose a privacy-preserving and truthful auction framework for task assignment in outsourced cloud environments. Theoretically, we analyze the complexity of our scheme in detail and prove the security in the presence of semi-honest adversaries. Finally, we evaluate the performance and feasibility of our scheme through a large number of simulation experiments.

Keyphrases: Auction, privacy preserving, task assignment, Yao's garbled circuits

@Booklet{EasyChair:9387,
  author = {Xufeng Jiang and Lu Li},
  title = {Privacy-Preserving and Truthful Auction for Task Assignment in Outsourced Cloud Environments},
  howpublished = {EasyChair Preprint no. 9387},

  year = {EasyChair, 2022}}