High Efficiency Privacy-Preserving Scheme for Data Distribution in Hybrid Cloud

Abstract

Since 2009, the amount of digital data being produced has doubled every year. Cloud computing storage services are an economical approach to dealing with such volumes of data. In cloud storage situations, data encryption and access control are commonly used for the protection of data. Full data encryption is able to ensure privacy; however, this approach makes it difficult to analyze the data in an efficient manner. Data analysis in a cloud environment requires a balance between data security and data analysis capacity. This study proposes a high-efficiency, privacy-preservation scheme for data distribution in hybrid clouds to improve data protection, expand the availability of data, and reduce cloud system resources consumption. Data classification and selective data protection mechanisms are also addressed. Results of functional analysis and comparison revealed that the proposed scheme reduces the time required for encryption and decryption by as much as 82.76% and decreases the number of privacy content by up to 92.5% while allowing data mining without compromising privacy.