Data science is the basis for various disciplines in the Big-Data era. Due to the high volume, velocity, and variety of big data, data owners often store their data in data servers. Past few years, many computation techniques have emerged to protect the security and privacy of such shared data while enabling analysis thereon. Hence, access-control systems must provide a fine-grained, multi-layer mechanism to protect data. However, the existing systems and frameworks fail to satisfy all these requirements and resolve the trust issue between data owners and analysts. In this paper, we propose SEAL as a framework to protect the security and privacy of shared data. SEAL enables computations on shared data while they remain under the complete control of data owners through pre-defined policies. Our framework employs the capability-object model to define flexible access policies. SEAL's access-control system supports delegating and revoking access privileges and other access-control customizations. In addition, SEAL can assign security labels to privacy-sensitive data and track them to enable data owners to define where and when a data analyst can access their data. We demonstrate the practicability of our approach by presenting a prototype implementation of SEAL. Furthermore, we display the flexibility of our framework by implementing multiple data-analytic scenarios, which cover different applications.
ACM SACMAT 2023