Mobile crowd sensing is a widely used sensing paradigm allowing applications on mobile smart devices to routinely obtain spatially distributed data on a range of user attributes: location, temperature, video and audio. Such data then typically forms the input to application specific machine learning tasks to achieve objectives such as improving user experience, targeting geo-localised query based searches to user interests and commercial aspects of targeted geo-localised advertising. We consider a scenario in which the sensing application purchases data from spatially distributed smartphone users. In many spatial monitoring applications, the crowdsourcer needs to incentivize users to contribute sensing data. This may help ensure collected data has good spatial coverage, which will enhance quality of service provided to the application user when used in machine learning tasks such as spatial regression. Privacy considerations should be addressed in such crowd sensing applications, and an incentive offered to “privacy-concerned” users to contribute data. A novel Stackelberg incentive mechanism is developed that allows workers to specify their location whilst satisfying their location privacy requirements. The Stackelberg and Nash equilibria are explored and an algorithm to demonstrate the approach is developed for a real data application.

移动人群感测是一种广泛使用的感测范式，允许移动智能设备上的应用程序常规获取一系列用户属性（位置，温度，视频和音频）上的空间分布数据。然后，此类数据通常形成对特定于应用程序的机器学习任务的输入，以实现诸如改善用户体验，将基于地理定位查询的搜索定位到用户兴趣和目标地理定位广告的商业方面的目标。我们考虑了一种情况，其中传感应用程序从空间分布的智能手机用户购买数据。在许多空间监控应用中，众包提供商需要激励用户贡献感测数据。这可能有助于确保收集的数据具有良好的空间覆盖范围，当在机器学习任务（例如空间回归）中使用时，这将提高为应用程序用户提供的服务质量。在此类人群感知应用程序中应解决隐私注意事项，并鼓励“关注隐私”的用户提供数据。开发了一种新颖的Stackelberg激励机制，该机制允许工作人员在满足其位置隐私要求的同时指定其位置。探索了Stackelberg和Nash平衡，并开发了一种用于证明该方法用于实际数据应用的算法。开发了一种新颖的Stackelberg激励机制，该机制允许工作人员在满足其位置隐私要求的同时指定其位置。探索了Stackelberg和Nash平衡，并开发了一种用于证明该方法用于实际数据应用的算法。开发了一种新颖的Stackelberg激励机制，该机制允许工作人员在满足其位置隐私要求的同时指定其位置。探索了Stackelberg和Nash平衡，并开发了一种用于证明该方法用于实际数据应用的算法。