A comprehensive performance evaluation of TEEs for confidential DNA alignment

Data confidentiality is crucial when processing sensitive information, often limiting user interactions and shared computing services like the cloud. While Trusted Execution Environments (TEEs) offer a means to ensure privacy in untrusted environments, they frequently introduce significant computational overhead. DNA alignment, a key step in bioinformatics workflows, is privacy-sensitive and computationally intensive. Given its parallelizable nature, it is a compelling case study for evaluating the performance impact and scalability of various TEEs. This study assesses three TEEs – Intel SGX, Intel TDX, and AMD SEV-SNP – by evaluating their overhead through real-world bioinformatics workloads and system-level microbenchmarks. Our evaluation shows that SGX-based solutions incur substantial overhead, particularly for small workloads, with slowdowns ranging from 283% to 1971% compared to native execution. The overhead is reduced for larger workloads, ranging from 15% to 57%. In contrast, TDX and SEV-SNP offer significantly improved performance: TDX limits overhead to 73% for small and to 9% for large workloads, while SEV-SNP incurs at most 67% and 29%, respectively. Importantly, SEV-SNP demonstrates better scalability than TDX, a result supported by microbenchmark analysis showing more efficient thread creation and scheduling. Conversely, TDX shows more efficient memory utilization, underscoring distinct overhead sources among the evaluated TEE architectures.