Microbenchmarking Cloud Cryptographic Workloads for Privacy-Preserving Healthcare IoT

2026-05-21

AI Summary

This study addresses the challenge of balancing cryptographic performance and cost in healthcare IoT cloud platforms while preserving privacy. It presents the first systematic microbenchmarking of widely used cryptographic primitives—SHA-HMAC, AES, ECC, and RSA—in serverless (FaaS) environments integrated with key management services (KMS) on AWS and Azure, across a multidimensional configuration space encompassing CPU architectures (x86_64 and Arm64), six programming languages, memory allocations, and instance types. The analysis uncovers performance patterns specific to healthcare IoT cryptographic workloads, identifies optimal software-hardware configurations, and proposes a deployment strategy that jointly optimizes latency and cost. This approach significantly enhances cloud-based encryption performance and resource efficiency, thereby supporting high-security, time-sensitive healthcare IoT applications.

Abstract

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.

Problem

• cloud cryptography
• healthcare IoT
• performance characterization
• cryptographic workloads
• privacy-preserving

Innovation

• microbenchmarking
• cryptographic workloads
• Function as a Service (FaaS)
• healthcare IoT
• cloud performance