The use of de-identification methods for secure and privacy-enhancing big data analytics in cloud environments

Big data analytics are interlinked with distributed processing frameworks and distributed database systems, which often make use of cloud computing services providing the necessary infrastructure. However, storing sensitive data in public clouds leads to security and privacy issues, since the cloud service presents a central point of attack for external adversaries as well as for administrators and other parties which could obtain necessary privileges from the cloud service provider. To enable data security and privacy in such a setting, we argue that solutions using de-identification methods are most suitable. Thus, this position paper presents the starting point for our future work aiming at the development of a privacy-preserving tool based on de-identification methods to meet security and privacy requirements while simultaneously enabling data processing.