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Intel SGX has been a popular trusted execution environment (TEE) forprotecting the integrity and confidentiality of applications running onuntrusted platforms such as cloud. However, the access patterns of SGX-basedprograms can still be observed by adversaries, which may leak importantinformation for successful attacks. Researchers have been experimenting withOblivious RAM (ORAM) to address the privacy of access patterns. ORAM is apowerful low-level primitive that provides application-agnostic protection forany I/O operations, however, at a high cost. We find that someapplication-specific access patterns, such as sequential block I/O, do notprovide additional information to adversaries. Others, such as sorting, can bereplaced with specific oblivious algorithms that are more efficient than ORAM.The challenge is that developers may need to look into all the details ofapplication-specific access patterns to design suitable solutions, which istime-consuming and error-prone. In this paper, we present the lightweight SGXbased MapReduce (SGX-MR) approach that regulates the dataflow of data-intensiveSGX applications for easier application-level access-pattern analysis andprotection. It uses the MapReduce framework to cover a large class ofdata-intensive applications, and the entire framework can be implemented with asmall memory footprint. With this framework, we have examined the stages ofdata processing, identified the access patterns that need protection, anddesigned corresponding efficient protection methods. Our experiments show thatSGX-MR based applications are much more efficient than ORAM-basedimplementations.

SGX-MR: Regulating Dataflows for Protecting Access Patterns of Data-Intensive SGX Applications