论文标题
私人，公平和可验证的总体统计数据，用于区块链时代的移动人群
Private, Fair, and Verifiable Aggregate Statistics for Mobile Crowdsensing in Blockchain Era
论文作者
论文摘要
在本文中，我们提出了Faircrowd，这是一个基于公共区块链的移动人拥护者的私人，公平且可验证的框架。在特定的情况下，激励移动用户收集和共享私人数据值（例如当前位置），以实现客户发布的一项常见的兴趣任务，而CrowdSensing Server计算了对移动用户（例如，最受欢迎的位置）的汇总统计信息。通过利用Elgamal加密，服务器几乎没有了解私人数据或统计结果。可以使用新的有效且可验证的计算方法公开验证聚合统计的正确性。此外，在贪婪的服务提供商，客户和移动用户在场的情况下，确保了激励措施的公平性，他们可能会启动付款，减少付款，自由骑行，双重报告和Sybil攻击以腐败奖励分配。最后，事实证明，Faircrowd可以实现可为移动用户提供隐私保护的可验证总统计信息。进行了广泛的实验，以证明Faircrowd在移动人群中的总统计数据的高效率。
In this paper, we propose FairCrowd, a private, fair, and verifiable framework for aggregate statistics in mobile crowdsensing based on the public blockchain. In specific, mobile users are incentivized to collect and share private data values (e.g., current locations) to fufill a commonly interested task released by a customer, and the crowdsensing server computes aggregate statistics over the values of mobile users (e.g., the most popular location) for the customer. By utilizing the ElGamal encryption, the server learns nearly nothing about the private data or the statistical result. The correctness of aggregate statistics can be publicly verified by using a new efficient and verifiable computation approach. Moreover, the fairness of incentive is guaranteed based on the public blockchain in the presence of greedy service provider, customers, and mobile users, who may launch payment-escaping, payment-reduction, free-riding, double-reporting, and Sybil attacks to corrupt reward distribution. Finally, FairCrowd is proved to achieve verifiable aggregate statistics with privacy preservation for mobile users. Extensive experiments are conducted to demonstrate the high efficiency of FairCrowd for aggregate statistics in mobile crowdsensing.
