Data Privacy Protection for Edge Computing of Smart City in a DIKW Architecture

Abstract

Current trend of shifting computing from centralized Cloud to Edge has not only empowered huge amount of IoT devices with the capability of the accumulation of individualized computing and storage as a flexible whole, but also brought along new privacy challenges originating in emerging new usage requests on the accumulated content or resources from multiple sources of various integrated devices at the Edge. In this work, we focus on modeling the privacy content of multiple sources through mapping them as resources of types of Data, Information and Knowledge in the well-known DIKW architecture. We propose to categorize content objects and relationships uniformly as typed resources of data, information, and knowledge, according to our formalized DIKW architecture composing a meta model of DIKW and extended Data Graph, Information Graph and Knowledge Graph. We further propose to categorize target privacy resources of data and information according to their presence in the modeled searching space in our DIKW architecture as explicit and implicit divisions. Thereafter we propose protection solutions according to explicit and implicit divisions for privacy target concerning typed data. The efficiency and performance potential of our processing solution originates in a multiple dimensional modeling strategy of typed data, which is modeled solely with various frequencies of various meta level dimensions.