Design of Big Data Privacy Framework—A Balancing Act

Abstract

Technological advancements in the field of Big Data and IoT have led to unprecedented growth in digital data. Data is collected from multiple distributed sources by business organizations, government agencies, and healthcare sectors. Data collected is mined to uncover valuable data, and the insights they provide are used by these organizations for optimized decision making. Data thus amassed may also contain sensitive personal information of individuals that are at risk of disclosure during analytics. Hence, there is a need for a privacy-aware system that enforces sensitive data protection. But such a system constrains the usefulness of data. Study shows that although significant findings do exist for balancing these contradicting objectives, the efficacy and scalability of these solutions continue to challenge the research community, given the volume of Big Data. Assessing the appropriate blend of these objectives for mutual benefit of organizations and customers requires leveraging the benefit of the modern tools and technologies in the Big Data ecosystem. This research study extensively reviews the previous work in the direction of privacy preserved Big Data analytics, and the review is first of its kind in exploring the challenges that have to be overcome in striking a balance between data value, privacy, scalability, and performance.