Microbenchmarking Cloud Cryptographic Workloads for Privacy-Preserving Healthcare IoT
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914593896398848 |
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| author | Webb, Jeremiah L. Kandel, Laxima Niure Gupta, Deepti Elluri, Lavanya |
| author_facet | Webb, Jeremiah L. Kandel, Laxima Niure Gupta, Deepti Elluri, Lavanya |
| contents | Cryptographic operations are an essential component of cloud security architectures; their comprehensive performance characterization across different cloud services, hardware architectures, and programming language implementations remains unknown. Specifically, healthcare IoT devices are highly vulnerable and frequently targeted, yet the cryptographic performance trade offs in their cloud security architectures remain poorly understood. This research presents an extensive microbenchmark study evaluating the performance of core cryptographic workloads, including SHA HMAC generation, AES encryption, decryption, Elliptic Curve Cryptography (ECC) signature generation and verification, and RSA encryption, decryption, across Function as a Service (FaaS) integrated with Key Management Services (KMS) from Amazon Web Services (AWS) and Microsoft Azure. We evaluate FaaS platforms using Elastic Compute Cloud (EC2) instances and Azure Virtual Machines, specifically using burst optimized instance types to analyze performance under typical cloud workload patterns. The benchmark encompasses a comprehensive multi dimensional analysis spanning two CPU architectures (x86 64 and Arm64), six widely adopted programming languages (Rust, Go, Python, Java, C#, and TypeScript), multiple memory allocation configurations, and diverse instance types to capture the complex interplay between these factors. This study identifies optimal configurations for cryptographic workloads in FaaS environments, improving performance and cost efficiency while enabling secure and timely data protection for healthcare IoT applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24063 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Microbenchmarking Cloud Cryptographic Workloads for Privacy-Preserving Healthcare IoT Webb, Jeremiah L. Kandel, Laxima Niure Gupta, Deepti Elluri, Lavanya Cryptography and Security Cryptographic operations are an essential component of cloud security architectures; their comprehensive performance characterization across different cloud services, hardware architectures, and programming language implementations remains unknown. Specifically, healthcare IoT devices are highly vulnerable and frequently targeted, yet the cryptographic performance trade offs in their cloud security architectures remain poorly understood. This research presents an extensive microbenchmark study evaluating the performance of core cryptographic workloads, including SHA HMAC generation, AES encryption, decryption, Elliptic Curve Cryptography (ECC) signature generation and verification, and RSA encryption, decryption, across Function as a Service (FaaS) integrated with Key Management Services (KMS) from Amazon Web Services (AWS) and Microsoft Azure. We evaluate FaaS platforms using Elastic Compute Cloud (EC2) instances and Azure Virtual Machines, specifically using burst optimized instance types to analyze performance under typical cloud workload patterns. The benchmark encompasses a comprehensive multi dimensional analysis spanning two CPU architectures (x86 64 and Arm64), six widely adopted programming languages (Rust, Go, Python, Java, C#, and TypeScript), multiple memory allocation configurations, and diverse instance types to capture the complex interplay between these factors. This study identifies optimal configurations for cryptographic workloads in FaaS environments, improving performance and cost efficiency while enabling secure and timely data protection for healthcare IoT applications. |
| title | Microbenchmarking Cloud Cryptographic Workloads for Privacy-Preserving Healthcare IoT |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2605.24063 |