Fog-Enabled Privacy-Preserving Multi-Task Data Aggregation for Mobile Crowdsensing

doi:10.1109/TDSC.2023.3277831

Fog-Enabled Privacy-Preserving Multi-Task Data Aggregation for Mobile Crowdsensing

2024-05-01

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DOI：10.1109/TDSC.2023.3277831

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Privacy-preserving data aggregation in mobile crowdsensing (MCS) focuses on mining information from massive sensing data while protecting users' privacy. The existence of multiple concurrent tasks is common in urban environments, so privacy-preserving multi-task data aggregation is essential and useful to a large-scale crowdsensing server. However, existing privacy-preserving data aggregation schemes in MCS mainly focus on the single-task data aggregation and the privacy protection of user's data. Little attention is paid to the privacy of user's decision of accepting tasks. Therefore, we propose a privacy-preserving and server-oriented efficient multi-task data aggregation scheme for MCS based fog computing. The proposed scheme can aggregate multiple concurrent tasks from multiple requesters (e.g., for 9 tasks, the proposed scheme completes all tasks in one round as opposed to existing schemes, which finish 9 tasks in nine rounds). Our scheme protects the privacy of user's decision, user's data, and aggregation result of each requester under collusion attacks. Through formal security analyses, our scheme is proved to be secure and privacy-preserving. Both theoretical analyses and experiments show our scheme is efficient.

Keywords:

Task analysis

Data aggregation

Servers

Data privacy

Crowdsensing

Multitasking

Edge computing

Mobile crowdsensing

privacy protection

data aggregation

multiple concurrent tasks

multi-secret sharing

fog computing

Fog-Enabled Privacy-Preserving Multi-Task Data Aggregation for Mobile Crowdsensing

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