PP-MAD: Privacy-preserving multi-task data aggregation in mobile crowdsensing via blockchain

• Published in: Computer standards and interfaces Vol. 94; p. 104002
• Main Authors: Yan, Xingfu, Ding, Jiaju, Luo, Fucai, Gong, Zheng, Ng, Wing W.Y., Luo, Yiyuan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2025
• Subjects: Chinese Remainder Theorem; Mobile crowdsensing; Privacy preserving; Secure aggregation; Secure aggregation; Mobile crowdsensing; Privacy preserving; Chinese Remainder Theorem
• ISSN: 0920-5489
• DOI: 10.1016/j.csi.2025.104002

In smart city, multi-task data aggregation has become a key method for extracting useful information from massive sensing data generated by concurrent mobile crowdsensing tasks from multiple task requesters. In such multi-requester and multi-task scenario, each task requester wants to protect the privacy of their own aggregation results. Thus, protecting privacies of both workers and task requesters pose a significant challenge for multi-task data aggregation. Most existing privacy-preserving data aggregation methods focus on single-requester scenarios. When applied to multi-task and multi-requester aggregation, existing methods are inefficient due to completing repeatedly each task and fail to safeguard the privacy of each task requester. Additionally, existing multi-task data aggregation schemes do not support multiple types of aggregation. To tackle these challenges, we propose PP-MAD, a multi-type and privacy-preserving multi-task data aggregation scheme based on blockchain for mobile crowdsensing. PP-MAD is able to aggregate multiple concurrent tasks from multiple task requesters, and it supports many types of data aggregation, including sum, mean, variance, weighted sum, weighted mean. Moreover, PP-MAD ensures privacies of workers’ data and aggregation results of each task requester, even under collusion attacks. A detailed security analysis verifies that PP-MAD is both secure and privacy-preserving. Furthermore, experimental results and theoretical analyses of both computation and communication costs demonstrate our scheme is efficient. •Aggregates multi-requester tasks while protecting worker data/decision privacy.•Ensures the privacy of each requester against collusion attacks.•Supports various aggregation types: sum, mean, variance, weighted sum/mean.