Privacy preserving data sharing and analysis for edge-based architectures

In this paper, we present a framework for privacy preserving collaborative data analysis among multiple data providers actingas edge of a cloud environment. The proposed framework computes the best trade-off among privacy and result accuracy,based on the privacy requirements of data providers and the specific requested analysis algorithm. Though the presented modelis general and can be applied to different environments, this work is motivated by the need of sharing information related toCyber Threats (CTI). The presented framework is independent from the number of data providers, used data format, privacyrequirement and analysis operations. The model is based on the concepts of trade-off score between accuracy and privacy,which also considers measures for privacy requirement such as differential privacy, l-diversity and k-anonymity. Togetherwith the model, the paper discusses the framework implementation and presents results to show the effectiveness and viabilityof the proposed approach.