Bigger is always not better, less is more, sometimes: the concept of
data minimization in the context of Big Data
Abstract
With the data landscape of the universe expands every second every
day by leaps and bound, the data value also increases unprecedentedly.
Particularly, the disruptive use of data in location tracking,
predictive policing, fraud detection, healthcare, advertising media, and
entertainment has already revitalized personal data in many ways. But
massive amassing of data also gives rise to new issues regarding the Big
Data effects, including privacy invasion, data breaches, and cyber
threats, etc. Taking effective efforts for mitigating the risks of data
explosion thus becomes indispensable for companies, organizations, and
societies alike. In such background, this paper attempts to focus on the
ways how the data minimization approach mitigates such risks, and how
this approach as a concept is being incorporated in legal instruments
globally. After exploring practical methods of applying data
minimization, the paper concludes by delineating the way out of the
existing dilemmas so created in the face of Big Data.
