The nowadays ubiquitous location-aware mobile devices have contributed to the rapid growth of individual-level location data. Such data are usually collected by location-based service platforms as training data to improve their predictive models' performance, but the collection of such data may raise public concerns about privacy issues. In this study, we introduce a privacy-preserving location recommendation framework based on a decentralized collaborative machine learning approach: federated learning. Compared with traditional centralized learning frameworks, we keep users' data on their own devices and train the model locally so that their data remain private. The local model parameters are aggregated and updated through secure multiple-party computation to achieve collaborative learning among users while preserving privacy. Our framework also integrates information about transportation infrastructure, place safety, and flow-based spatial interaction to further improve recommendation accuracy. We further design two attack cases to examine the privacy protection effectiveness and robustness of the framework. The results show that our framework achieves a better balance on the privacy–utility trade-off compared with traditional centralized learning methods. The results and ensuing discussion offer new insights into privacy-preserving geospatial artificial intelligence and promote geoprivacy in location-based services.

中文翻译：

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基于去中心化协作机器学习的位置推荐隐私保护框架

如今无处不在的位置感知移动设备促进了个人位置数据的快速增长。此类数据通常由基于位置的服务平台收集作为训练数据以提高其预测模型的性能，但此类数据的收集可能会引起公众对隐私问题的担忧。在这项研究中，我们引入了一个基于去中心化协作机器学习方法的隐私保护位置推荐框架：联邦学习。与传统的集中式学习框架相比，我们将用户的数据保存在他们自己的设备上，并在本地训练模型，从而使他们的数据保持私密。通过安全的多方计算聚合和更新本地模型参数，在保护隐私的同时实现用户之间的协作学习。我们的框架还集成了有关交通基础设施、场所安全和基于流的空间交互的信息，以进一步提高推荐准确性。我们进一步设计了两个攻击案例来检验框架的隐私保护有效性和鲁棒性。结果表明，与传统的集中式学习方法相比，我们的框架在隐私-效用权衡方面取得了更好的平衡。结果和随后的讨论为保护隐私的地理空间人工智能提供了新的见解，并促进了基于位置的服务中的地理隐私。我们进一步设计了两个攻击案例来检验框架的隐私保护有效性和鲁棒性。结果表明，与传统的集中式学习方法相比，我们的框架在隐私-效用权衡方面取得了更好的平衡。结果和随后的讨论为保护隐私的地理空间人工智能提供了新的见解，并促进了基于位置的服务中的地理隐私。我们进一步设计了两个攻击案例来检验框架的隐私保护有效性和鲁棒性。结果表明，与传统的集中式学习方法相比，我们的框架在隐私-效用权衡方面取得了更好的平衡。结果和随后的讨论为保护隐私的地理空间人工智能提供了新的见解，并促进了基于位置的服务中的地理隐私。