Efficient Privacy-preserving Aggregation for Mobile Crowdsensing

Mobile crowdsensing applications can learn the aggregate statistics over personal data to produce useful knowledge about the world. Since personal data may be privacy-sensitive, the aggregator should only gain desired statistics without learning anything about the personal data. Differential privacy, the state-of-the-art privacy mechanism, can provide strong protection to ensure parties' privacy in such scenarios. Correspondingly, based on the differential privacy, many collusion-tolerant aggregation schemes have been proposed. However, those collusion-tolerant schemes usually incur high accumulated error and also require the priori knowledge of the fraction of those colluded parties. In this paper, we propose a differential-private collusion-tolerant aggregation protocol, while incurring no additionally error except the noise required for providing the differential privacy guarantee. Another salient characteristic of the proposed protocol is that it need not to have an priori estimation of those colluded parties. In addition, we also design an efficient aggregation encryption scheme to support those mobile crowdsensing applications where large plaintext is required. We also make some extensions to make the proposed protocol more applicable in realities, such as the fault tolerant. The analysis shows that the proposed protocol can achieve desired goals, and the performance evaluation demonstrates the protocol's efficiency in reality.