REAPP: A low-cost and accurate reputation evaluation based anonymous privacy preserving scheme in mobile crowdsourcing

Zeyuan Li, Yinghao Yao, Anfeng Liu, Neal N. Xiong, Shaobo Zhang, Athanasios V. Vasilakos

Abstract: Due to sensitive data, reputation concerns, and uncertain worker behaviors, it is essential for practical Mobile Crowd Sensing (MCS) to preserve privacy and ensure high-quality data when recruiting workers. In this paper, a low-cost and accurate reputation evaluation-based anonymous privacy preserving (REAPP) scheme is proposed to improve data quality and reduce cost for MCS. The important components and innovative aspects of the REAPP scheme are as follows. 1) A low-cost and accurate reputation evaluation (LARE) approach is proposed to select highly trusted workers and obtain high-quality data at a lower cost. The LARE approach utilizes data reported by trusted workers to evaluate the reputation of other workers, and a matrix factorization-based data completion (MFDC) algorithm is adopted to reduce data collection costs. 2) Multilayer linkable spontaneous anonymous group signatures and Paillier encryption are employed in blockchain to conceal workers’ real identities, thereby preserving their reputation and identity privacy. 3) Pedersen commitment and Schnorr signature are adopted to ensure that workers and DR can engage in private transactions and verify their validity, thus protecting the privacy of participants. 4) Proxy re-encryption method is employed to preserve the data of recruited workers from being accessed by unrelated third parties, while reducing costs by not recruiting low-trust workers. Finally, the proposed REAPP scheme is theoretically proven to be correct and effective. Simulations based on real-world datasets illustrate that our REAPP scheme outperforms the state-of-the-art methods.