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BidGuard: A framework for privacy-preserving crowdsensing incentive mechanisms

机译：BidGuard：保护隐私的众包奖励机制的框架

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摘要

With the rapid growth of smartphones, crowdsensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Auction has been widely used to design mechanisms to stimulate smartphone users to participate in the crowdsensing applications and systems. Many auction-based incentive mechanisms have been proposed for crowdsensing. However, none of them has taken into consideration both the bid privacy of smartphone users and the social cost. To the best of our knowledge, we are the first to study the design of privacy-preserving incentive mechanisms that also achieve approximate social cost minimization. In this paper, we design BidGuard, a general privacy-preserving framework for incentivizing crowdsensing. This framework works with different score functions for selecting users. In particular, we propose two score functions, linear and log functions, to realize the framework. We rigorously prove that BidGuard achieves computational efficiency, individual rationality, truthfulness, differential privacy and approximate social cost minimization. In addition, the BidGuard with log score function is asymptotically optimal in terms of the social cost. Extensive simulations evaluate the performance and validate the desired properties of BidGuard.
机译：随着智能手机的快速增长，众群地涌现为一种新的范式，利用普遍的传感器嵌入的智能手机来有效地收集数据。拍卖已广泛用于设计刺激智能手机用户参与众持应用程序和系统的机制。已经提出了许多基于拍卖的激励机制来占有众群。但是，他们都没有考虑智能手机用户的竞标和社会成本。据我们所知，我们是第一个研究隐私保留激励机制设计的设计，也实现了近似的社会成本最小化。在本文中，我们设计了一个普遍的隐私保留框架，用于激励众群的普遍存在框架。此框架适用于选择用户的不同分数功能。特别是，我们提出了两个分数函数，线性和日志功能，以实现框架。我们严格证明，Bidguard实现了计算效率，个人合理性，真实性，差异隐私和近似的社会成本最小化。此外，在社会成本方面，带有日志得分功能的Bidguard是渐近的最佳状态。广泛的模拟评估性能并验证出价的所需属性。

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