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    基于区块链的群智感知双向信誉评估隐私保护

    Privacy-Preserving Bilateral Reputation Evaluation in Blockchain Based Crowdsensing

    • 摘要: 在移动群智感知系统中,信誉管理是提高数据收集质量的有效途径,但现有基于主观评价的方案难以实现隐私保护下的工人信誉真实评估. 为此,提出了一种基于区块链的分布式群智感知双向信誉评估隐私保护(bilateral reputation evaluation privacy-preserving,BREPP)方案. 首先采用随机干扰、佩德森承诺隐藏工人信誉更新的参数,利用紧凑型可链接自发匿名群签名匿名验证了信誉更新过程,联合Bulletproofs范围证明与Schnnor签名验证了工人的信誉评估过程;然后,采用子地址方法实现工人和请求者身份匿名下的快速定位检索;最后在Hyperledger Fabric上部署了实验原型系统并进行了性能评估. 结果表明,BREPP方案与已有信誉隐私保护方案相比,信誉管理总时延和信誉评估误差率分别至少降低了2.00个百分点和24.96个百分点;与无隐私方案相比,在信誉评估准确率与总时延之间取得了较好均衡.

       

      Abstract: Reputation management is an effective way to improve the quality of data collection in mobile crowdsensing, but existing subjective evaluation-based schemes are difficult to realize the real evaluation of worker reputation under privacy protection. To this end, we propose a bilateral reputation evaluation privacy-preserving (BREPP) scheme based on blockchain. The scheme firstly adopts random interference and Pedersen Commitment to hide the parameters of the workers’ reputation update, and utilizes the compact linkable spontaneous anonymous group (CLSAG) signature to anonymously verify the reputation update process. Besides, it employs a combination of Bulletproofs and Schnnor signature to verify the reputation feedback process of the workers. Then, the sub-address method is adopted to achieve fast location retrieval when the identities of the workers and requesters are anonymized. Finally, a prototype system is deployed on Hyperledger Fabric to evaluate the performance of the reputation update. The simulation results show that compared with existing reputation privacy protection schemes, BREPP scheme reduces the total reputation management delay and reputation evaluation error rate by at least 2.00% and 24.96%, respectively. Compared with schemes without privacy protection, our scheme achieves a better balance between reputation evaluation accuracy and total delay.

       

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