Edge-based Differenital Big Data Processing for Sensor-Cloud Systems

The rise of big data processing and storage bring new challenges for privacy protection. More data, especially personal information, being hosted online such as in cloud, which is out of control. In this paper, we propose an edge-based model for big data processing, taking sensor-cloud data as an example, in which the raw data from wireless sensor networks (WSNs) is differentially processed by algorithms on edge servers. A small quantity of core data is stored on edge and local servers while the rest is transmitted to cloud for storage. In this way, the benefits are twofold. First, the data privacy is preserved since the original data cannot be retrieved even if the data stored in cloud is leaked. Second, implemented by a differential storage method, compared to the state of the art, the edge-based model sends less data to the cloud and reduces the cost of communication and storage. Both theoretical analyses and extensive experiments validate our proposed method.