Privacy-preserving Data Aggregation Scheme in Vehicular CrowdSensing
In Vehicular CrowdSensing(VCS),the transmission efficiency can be improved by aggregating perception data.Because the aggregator is not trusted,there is a risk of privacy leakage of sensitive information such as identity and location contained in the perception data and aggregation results.Existing schemes using homomorphic encryption or bilinear encryption to achieve privacy pro-tection have large computing and communication costs.To address the above issues,a privacy protected data aggregation scheme in VCS is proposed,which uses data mask technology based on elliptic curve non-interactive key negotiation to encrypt perception data and aggregation results to achieve privacy protection;Using lightweight elliptic curve signature algorithms to sign data and aggregate results to reduce computational overhead;Design an aggregation algorithm for perceptual data based on the Homer criterion and the Chinese remainder theorem to reduce communication costs.Security analysis proves that this scheme ensures the privacy,integrity,and authentication of perception data and aggregation results;Performance analysis shows that compared to the MSLPDA(Modified Safe and Lightweight Privacy-preserving Data Aggregation)based on bilinear mapping and the MPPDA(Modified Privacy-preserving Data Aggregation)based on homomorphic encryption,the proposed scheme has significantly lower computational overhead in the data col-lection,data aggregation,and result analysis stages,and the end-to-end communication overhead is much lower than that of the MSLP-DA and MPPDA schemes.
internet of vehiclecrowdsensingdata aggregationprivacy protection
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