Privacy Protection Incentive Mechanism Based on User-Union Matching in Mobile Crowdsensing

As a novel Internet of things (IoT) sensing mode, mobile crowdsensing provides a new way and means for ubiquitous social perception. A large number of sensing data containing sensitive and private information of sensing users is gathered in the mobile crowdsensing, and a great deal of valuable information can be mined, which greatly increases the risk of hacker attacks and private data leakage. While encouraging more sensing users to participate in sensing tasks and providing real data, how to better protect the privacy of sensing data and sensing platform has become a prominent and pressing key issue. In order to solve the above problems, this paper proposes a user-union matching scheme based on the Bloom filter. Before the sensing users upload the sensing data who can choose using the Bloom filter and the binary product of the confusion vector to estimate the similarity, and effectively protect personal privacy information. Meanwhile, aiming at the efficiency of the private set intersection of the sensing data, this study puts forward a light-weight private sensing data set intersection protocol, which can realize private sensing data intersection operation without leakage of any user’s real sensing data. Furthermore, we propose a reputation-aware incentive mechanism based on user-union matching, which can effectively control the budget expenditure on the basis of improving the processing efficiency of sensing tasks. Finally, the security analysis shows that the proposed user-union matching scheme is provably secure, and the proposed private sensing data set intersection protocol is secure, and the performance analysis and experimental results show that the proposed reputation-aware incentive mechanism is efficient and effective.