Optimized CPU Execution Time Modeling of Compression–Encryption Combinations Across Different Security Levels over Mobile Cloud Scenario

Mobile users in mobile cloud environment necessitate multiple data security levels due to resource constraints, data sensitivity, and cyber threats. This study analyzed multiple security levels while optimizing energy consumption in a mobile cloud setting. This was accomplished through CPU execution time models based on various combinations of compression and encryption algorithms. The integration of encryption algorithms such as AES and ECC-AES, as well as compression algorithms such as RLE and Huffman were considered in this study. Text files ranging from 100 to 999 KB were compressed first and then encrypted to obtain the CPU execution time utilized. C# framework was used for the data profiling and RStudio was used for data analysis. AES-Huffman was determined to be the best combination for apps with faster execution needs, and ECC-AES-Huffman as the best option for apps with higher security needs. Different security levels, compression levels, energy consumption rates and optimized CPU execution time models were identified for each algorithm combinations. The AES variants such as AES128-Huffman, AES192-Huffman, AES256-Huffman combinations consumed CPU execution time of 1.92 ms, 4.08 ms and 6.33 ms, respectively. ECC-AES variants such as ECC-AES128-Huffman, ECC-AES192-Huffman, ECC-AES256-Huffman combinations consumed CPU time of 9.58 ms, 19.75 ms and 29.75 ms, respectively. The results confirmed that the increase in security levels led to increase in CPU execution time. Additionally, this study identified the greater compression efficiency of Huffman compression algorithm, compared to RLE. The results of this study may be used to develop a user-initiated costing option to choose mobile user’s preference of security level, compression level and energy consumption rates.