Open Access

ARTICLE

PARE: Privacy-Preserving Data Reliability Evaluation for Spatial Crowdsourcing in Internet of Things

Peicong He, Yang Xin^*, Yixian Yang

School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, 100876, China

* Corresponding Author: Yang Xin. Email:

(This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)

Computers, Materials & Continua 2024, 80(2), 3067-3084. https://doi.org/10.32604/cmc.2024.054777

Received 06 June 2024; Accepted 14 July 2024; Issue published 15 August 2024

Abstract

The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain and Paillier homomorphic cryptosystem, providing unchangeable and traceable data while overcoming privacy concerns. Secondly, based on the uploaded data, we propose a method to determine the approximate correct value region without knowing the exact value. Finally, we offer a data filtering mechanism based on the Paillier cryptosystem using this value region. The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection.

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Keywords

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