Another Look at Secure Big Data Processing: Formal Framework and a Potential Approach

Lei Xu, Pham Dang Khoa, Seung-Hun Kim, Won Woo Ro, Weidong Shi

Abstract: Big data comprises high-volume, high-velocity, and high-variety information assets that demand cost effective and innovative forms of information processing for enhanced insight and decision making. The rise of cloud computing makes providing flexible computation, communication, and storage capacity possible. Due to the outsourcing and sharing feature of cloud computing, security becomes one of the main concerns. Both the data and program are potential targets for security compromise. These concerns hinder the end users to migrate to the cloud for big data processing. A lot of techniques have been developed to alleviate the security concerns for big data processing in the cloud environment. However, these approaches usually only focus on data protection or rely on certain security anchor in the cloud environment. We propose a formal framework and security definition of big data processing which takes both data and program protection into consideration. The framework/security definition captures the key features of the scenario and avoids sinking into unnecessary details. We develop a solution under this framework which combines operation steganography and FHE scheme to satisfy the security definition.