Big Data Privacy: Challenges to Privacy Principles and Models

摘要

Abstract This paper explores the challenges raised by big data in privacy-preserving data management. First, we examine the conflicts raised by big data with respect to preexisting concepts of private data management, such as consent, purpose limitation, transparency and individual rights of access, rectification and erasure. Anonymization appears as the best tool to mitigate such conflicts, and it is best implemented by adhering to a privacy model with precise privacy guarantees. For this reason, we evaluate how well the two main privacy models used in anonymization ( k -anonymity and $$arepsilon $$ ε -differential privacy) meet the requirements of big data, namely composability, low computational cost and linkability.