Enhancing Selectivity in Big Data

Mathias Lécuyer, Riley Spahn, Roxana Geambasu, Tzu-Kuo Huang, Siddhartha Sen

Abstract: Today’s companies collect immense amounts of personal data and enable wide access to it within the company. This exposes the data to external hackers and privacy-transgressing employees. This study shows that, for a wide and important class of workloads, only a fraction of the data is needed to approach state-of-the-art accuracy. We propose selective data systems that are designed to pinpoint the data that is valuable for a company’s current and evolving workloads. These systems limit data exposure by setting aside the data that is not truly valuable.