ESMAC: Efficient and Secure Multi-Owner Access Control With TEE in Multi-Level Data Processing

Abstract

Traditional data access control schemes only prevent unauthorized access to private data with a single owner. They are not suitable for application in a Multi-Level Data Processing (MLDP) scenario, where data are processed by a series of parties who also insert new data. Hence, the accumulated dataset should be protected through access control handled by hierarchically-structured parties who are at least partial data owners in MLDP. Existing multi-owner access control schemes mainly focus on controlling access to co-owned data of multiple entities with the equal ownership, but seldom investigates how to apply access control in MLDP. In this paper, we base the off-the-shelf Trusted Execution Environment (TEE), Intel SGX, to propose an Efficient and Secure Multi-owner Access Control scheme (ESMAC) for access authorization in MLDP. Moreover, to prevent unauthorized data disclosure by non-root data owners aiming to gain extra profits, we further introduce undercover polices to supervise their behaviors. Specifically, we design a data protection scheme based on game theory to decide the payoffs and punishments of honest and dishonest data owners, which motivates data owners to behave honestly when claiming ownership over data. Through comprehensive security analysis and performance evaluation, we demonstrate ESMAC's security and effectiveness.