SGX-MR: Regulating Dataflows for Protecting Access Patterns of Data-Intensive SGX Applications
Proceedings on Privacy Enhancing Technologies
Intel SGX has been a popular trusted execution environment (TEE) for protecting the integrity and confidentiality of applications running on untrusted platforms such as cloud. However, the access patterns of SGX-based programs can still be observed by adversaries, which may leak important information for successful attacks. Researchers have been experimenting with Oblivious RAM (ORAM) to address the privacy of access patterns. ORAM is a powerful low-level primitive that provides application-agnostic protection for any I/O operations, however, at a high cost. We find that some application-specific access patterns, such as sequential block I/O, do not provide additional information to adversaries. Others, such as sorting, can be replaced with specific oblivious algorithms that are more efficient than ORAM. The challenge is that developers may need to look into all the details of application-specific access patterns to design suitable solutions, which is time-consuming and error-prone. In this paper, we present the lightweight SGX based MapReduce (SGX-MR) approach that regulates the dataflow of data-intensive SGX applications for easier application-level access-pattern analysis and protection. It uses the MapReduce framework to cover a large class of data-intensive applications, and the entire framework can be implemented with a small memory footprint. With this framework, we have examined the stages of data processing, identified the access patterns that need protection, and designed corresponding efficient protection methods. Our experiments show that SGX-MR based applications are much more efficient than the ORAM-based implementations.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Alam, A.K.M. Mubashwir; Sharma, Sagar; and Chen, Keke, "SGX-MR: Regulating Dataflows for Protecting Access Patterns of Data-Intensive SGX Applications" (2020). Clinical Lab Sciences Faculty Research and Publications. 56.
ADA Accessible Version
Published version. Proceedings on Privacy Enhancing Technologies, Vol. 2021, No. 1 (November 8, 2020): 5-20. DOI. © 2021 A K M Mubashwir Alam et al., published by Sciendo. Used with permission.