Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges

摘要

The increasing volume of personal and sensitive data being harvested by data controllers makes it increasingly necessary to use the cloud not just to store the data, but also to process them on cloud premises. However, security concerns on frequent data breaches, together with recently upgraded legal data protection requirements (like the European Union's General Data Protection Regulation), advise against outsourcing unprotected sensitive data to public clouds. To tackle this issue, this survey covers technologies that allow privacy-aware outsourcing of storage and processing of sensitive data to public clouds. Specifically and as a novelty, we review masking methods for outsourced data based on data splitting and anonymization, in addition to cryptographic methods covered in other surveys. We then compare these methods in terms of operations supported on the masked outsourced data, overhead, accuracy preservation, and impact on data management. Furthermore, we list several research projects and available products that have materialized some of the surveyed solutions. Finally, we identify outstanding research challenges.