TY  - EJOU
AU  - Li, Jie 
AU  - Song, Fuyuan 
AU  - Jiang, Qin 

TI  - Privacy-Preserving Collaborative Task Allocation for Multi-Skill Mobile Crowdsensing
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - Mobile crowdsensing enables large-scale sensing tasks through smart devices carried by users and has been widely applied in intelligent transportation and environmental monitoring. With the increasing complexity of sensing tasks, many tasks require the collaboration of multiple workers with different skills. However, both task-required skills and worker skills are privacy-sensitive, and directly exposing them to the platform may reveal task intentions and workers’ capability profiles. To address this issue, this paper proposes Dual-Fog Privacy-Preserving Multi-skill Task Allocation (DPMTA), a privacy-preserving task allocation scheme for multi-skill collaborative tasks. DPMTA adopts a dual-fog architecture to separately protect location privacy and skill privacy. Specifically, task and worker locations are perturbed by differential privacy, while skill information is split by XOR secret sharing and distributed to two non-colluding fog servers. With Beaver triple-assisted secure Boolean computation, DPMTA enables skill matching, coverage updating, and collaborative worker selection without revealing plaintext skills. In addition, a random permutation mechanism is introduced to hide the direct mapping between skill bit positions and semantic skill labels. Security analysis shows that DPMTA effectively protects skill and location privacy. Meanwhile, the experimental evaluation demonstrates that DPMTA maintains a high task allocation success rate while keeping communication and computation overhead within acceptable limits.
KW  - Privacy-preserving; task allocation; secret sharing; mobile crowdsensing (MCS)

DO  - 10.32604/cmc.2026.085946