PRIVAaaS: Privacy Approach for a Distributed Cloud-Based Data Analytics Platforms

Abstract

Data privacy is a key challenge that is exacerbated by Big Data storage and analytics processing requirements. Big Data and Cloud Computing are related and allow the users to access data from any device, making data privacy essential as the data sets are exposed through the web. Organizations care about data privacy as it directly affects the confidence that clients have that their personal data are safe. This paper presents a data privacy approach - PRIVAaaS - and its inte-gration to the LEMONADE Web-based platform, developed to compose ETL (Extract, Transform, Load) process and Machine Learning workflows. The 3-level approach of PRIVAaaS, based on data anonymization policies, is implemented in a software toolkit that provides a set of libraries and tools which allows controlling and reducing data leakage in the context of Big Data processing.