A privacy-preserving framework for location recommendation using decentralized collaborative machine learning

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.

Automated summary

The study introduces a privacy-preserving location recommendation framework based on federated learning, which keeps user data private by storing it on their devices and aggregates local model parameters securely through multiple-party computation. The framework also integrates transportation infrastructure, place safety, and flow-based spatial interaction information to improve recommendation accuracy.