Real-time Private Data Aggregation over Distributed Spatial-temporal Infinite Streams with Local Differential Privacy

With the continued proliferation of wireless communication and mobile devices equipped with built-in GPS sensors, burgeoning applications of location-based services are springing up, such as mobile crowdsourcing applications (MCS), which are revolutionizing our daily lives. However, collecting and sharing continuously spatio-temporal data to a service provider of MCS applications will incur users' concerns about their privacy. In this paper, we study the problem of locally differentially private data aggregation over distributed spatial-temporal infinite streams. To this end, we proposed a LDP-based framework for dealing with the problem. Firstly, we propose a novel model of (w, ε)-Clustering-based Local Differential Privacy ((w, ε)-CLDP) to capture the temporal and spatial correlations in spatio-temporal infinite stream while guaranteeing stringent differential privacy. Secondly, we develop an efficient GRR-based Local Budget Absorption (LBA) mechanism as a building block for achieving (w, ε)-CLDP and present its privacy analysis. On this basis, we present a framework of real-time spatio-temporal data aggregation over distributed infinite streams with an untrusted server. Lastly, we conduct experiments on two real-world datasets to validate our framework. The results manifest that the LBA-based framework is optimal in data utility for real-time spatio-temporal data aggregation with a rigorous privacy guarantee.