Adaptive Differentially Private Data Stream Publishing in Spatio-temporal Monitoring of IoT

Spatio-temporal monitoring of the Internet of Things (IoT) has enabled the development and proliferation of third-party computing services by extensively exploiting the massive amount of sensing data. In particular, continuously generated data stream are monitored in real-time and exploited to facilitate people's daily lives, such as traffic monitoring and epidemic prevention. In its simplest way of deployment, the direct publishing of various streams could seriously compromise the privacy of participating users. Hence, a more sophisticated scheme is needed to regulate the privately publishing of data streams, which may possibly require control to be applied dynamically. However, most existing solutions are non-adaptive to dynamic changes of the streams due to constraints of predefined parameters, thus are vulnerable to low data utility. In this paper, we present AdaPub, a data-adaptive framework for infinite multidimensional stream real-time publishing with ω-event differential privacy while ensuring high data utility. Without predefining the parameters, AdaPub could learn and update the parameters that reflect the spatio-temporal correlations of the stream in a data-adaptive manner. Specifically, we propose two modules DimParti and AdaCluster which are seamlessly incorporated into AdaPub to simultaneously learn dimension correlations and time correlations in a data-adaptive way, thus greatly improving the data utility of the sanitized streams. Extensive experiments on real-world datasets demonstrate that our solution substantially outperforms state-of-the-art solutions with much lower errors while achieving strong privacy guarantees.