An Efficient Privacy-Preserving Incentive Scheme without TTP in Participatory Sensing Network
This addresses the problem of motivating user participation while ensuring privacy and security in participatory sensing networks, which is incremental as it builds on existing cryptographic techniques.
The paper tackles the lack of incentive mechanisms, security, and privacy in participatory sensing networks by constructing an efficient privacy-preserving incentive scheme without a trusted third party, which allows participants to earn credits privately and is shown to be secure and more efficient in performance evaluations.
Along with the development of wireless communication technology, a mass of mobile devices are gaining stronger sensing capability, which brings a novel paradigm to light: participatory sensing networks (PSNs). PSNs can greatly reduce the cost of wireless sensor networks, and hence are becoming an efficient way to obtain abundant sensing data from surrounding environment. Therefore, PSNs would lead to significant improvement in various fields, including cognitive communication. However, the large-scale deployment of participatory sensing applications is hindered by the lack of incentive mechanism, security and privacy concerns. It is still an ongoing issue to address all three aspects simultaneously in PSNs. In this paper, we construct an efficient privacy-preserving incentive scheme without trusted third party (TTP) for PSNs to motivate user-participation. This scheme allows each participant to earn credits by contributing data privately. Using blind and partially blind signatures, the proposed scheme is proved to be secure for privacy and incentive. Additionally, the performance evaluation in terms of computation and storage indicates that the proposed scheme has higher efficiency.