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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework.

Chathura Widanage¹, Weijie Liu¹, Jiayu Li¹

¹Indiana University.

Proceedings. IEEE International Conference on Cloud Computing

|June 6, 2022

Summary

This summary is machine-generated.

We introduce HySec-Flow, a framework enhancing secure genomic data analysis on Intel SGX platforms. By partitioning tasks, it overcomes enclave memory limits, improving performance for large-scale computations.

Keywords:

Privacy-preserving Computing Reads mapping Software Guard Extension (SGX)

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Area of Science:

Computer Science
Bioinformatics
Cybersecurity

Background:

Trusted Execution Environments (TEEs) like Intel's Software Guard Extension (SGX) offer enhanced security for sensitive data, including human genomics.
A key limitation of SGX is the constrained enclave memory, hindering large-scale data processing.

Purpose of the Study:

To propose a novel framework, HySec-Flow, for efficient and secure large-scale genomic data analysis on SGX platforms.
To address the performance bottleneck caused by limited enclave memory in SGX.

Main Methods:

Developed HySec-Flow, a hybrid framework partitioning data-intensive genomic tasks into independent subtasks.
Deployed subtasks into secured and non-secured containers for parallel execution, utilizing Scorn and Graphene for legacy code porting.
Implemented a workflow for indexing, alignment, dispatching, and merging SGX-enabled container executions.

Main Results:

Performance evaluation using real human genome sequencing data demonstrated significant enhancement.
Partitioning time-consuming genomic computations into subtasks improved performance compared to conventional enclave execution.
The framework effectively alleviates the limited Page Cache (EPC) memory constraints.

Conclusions:

HySec-Flow provides a scalable and privacy-preserving solution for large-scale genomic data analysis on SGX.
The open-source framework is adaptable for other data-parallel computations requiring security and computational resources.
This approach enhances the feasibility of secure, high-throughput genomic data processing.