Privacy-Preserving Worker Selection Scheme Based on Support-Degree Weighted Aggregation

Aiming at the problem of unilateral privacy leakage of participants in the selection of workers in the MCS (Mobile Crowdsensing) voting scenario, and in order to improve the fairness and transparency of worker selection, this paper designs a privacy-preserving support-degree weighted aggregation protocol, combines it with the blockchain, and uses the support-degree weighted voting method to vote for workers with high support degrees, thus proposing a privacy-preserving worker selection scheme based on support-degree weighted aggregation. Firstly, the BGN homomorphic encryption technology is used to encrypt the support degrees of all workers participating in the task by the participants. Secondly, the variance and weighted sum of the encrypted support degrees are calculated on the ciphertext. Finally, the aggregated results of the support degrees of each worker are decrypted to ensure the privacy protection of the voting content of the participants throughout the whole process.