Title: Privacy-preserving data deduplication in edge-assisted mobile crowdsensing

Authors: Yili Jiang; Kuan Zhang; Yi Qian; Rose Qingyang Hu

Addresses: Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE, USA ' Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE, USA ' Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE, USA ' Department of Electrical and Computer Engineering, Utah State University, Logan, UT, USA

Abstract: Mobile crowdsensing enables the collaborative data collection between mobile workers and centralised cloud server. When sensing data from the surrounding environment, workers in the same location may generate the identical data report. Although edge intelligence is integrated to remove the redundant data by comparing the report content, disclosing the sensing data to the edge nodes results in severe privacy leakage. To detect and remove duplicated data without revealing the content, encryption-based data deduplication schemes are the main solutions. However, the existing schemes have high computational cost due to heavy cryptographic primitives. In this work, we propose a pairing-based data deduplication scheme with lower computational cost. The proposed scheme guarantees both secure data deduplication and secure contributor identification. In addition, by deploying proxy re-encryption, the privacy of task location is preserved. The experimental results demonstrate that the proposed scheme achieves better computational efficiency than the other schemes.

Keywords: privacy preservation; data deduplication; edge intelligence; computational efficiency; mobile crowdsensing; MCS.

DOI: 10.1504/IJMIS.2022.121283

International Journal of Multimedia Intelligence and Security, 2022 Vol.4 No.1, pp.1 - 19

Received: 18 Oct 2021
Accepted: 13 Dec 2021
Published online: 03 Mar 2022 *

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