simulated cryptographic benchmark dataset

Simulated Cryptographic Benchmark Dataset – Description This dataset is artificially created to emulate real-world encryption and decryption scenarios without relying on actual sensitive or proprietary data. It is used to systematically benchmark cryptographic algorithms such as AES-256, RSA-2048, ECC-256, etc. based on various performance and resource usage metrics. Key characteristics include: Synthetic Data Files: Binary files of varying sizes (e.g., 1 KB to 100 MB) are generated using secure random number functions (like os.urandom() in Python) to simulate diverse payloads. Controlled Environment: All algorithms are tested in a standardized environment (testbed), ensuring reproducibility and consistency in results. Recorded Metrics: Metrics typically include: Encryption/Decryption Speed (MB/s) CPU Usage (%) Memory Consumption (MB) No Sensitive Information: The dataset does not contain any real user or confidential data, making it safe for open use and public reporting. Benchmark Purpose: Designed for comparative evaluation across symmetric and asymmetric algorithms to analyze their scalability, efficiency, and suitability for deployment in environments like cloud, IoT, and edge devices. This kind of dataset is essential for empirical cryptographic research, offering insights into algorithm performance under varied conditions without the legal or ethical complications of handling real-world data.