Integrated File Compression and Encryption : Optimizing Security and Efficiency in Data Handling
Shulgin, Egor (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025061122292
https://urn.fi/URN:NBN:fi:amk-2025061122292
Tiivistelmä
In the era of big data, the demands for both storage efficiency and security require integrated approaches to compression and encryption. Traditional methods often compromise either the compression ratio or cryptographic strength, and many existing tools are not well adapted for various data types. This research overcomes these shortcomings by developing a modular system that simultaneously optimizes both processes while balancing performance and security.
The study proposes an integrated compression-encryption system, a Python-based framework combining DEFLATE compression and AES-256 encryption. Theoretical foundations are based on hybrid algorithms and secure key management, and a unidirectional workflow ensures data integrity. The methodology includes benchmarking on metrics such as compression ratio, throughput and memory utilization, tested on high-performance hardware under controlled conditions.
The results demonstrate DEFLATE’s superiority in speed and AES-256’s cryptographic efficiency, achieving 99.9% compression ratios. LZMA excels in compression depth but demands excessive memory, limiting edge-device applicability. The key conclusions advocate DEFLATE + AES-256 for time-sensitive tasks and highlight metadata inflation as a critical bottleneck. Future work should explore hybrid pipelines and metadata-efficient formats to enhance usability and resource allocation. This research provides actionable recommendations for industries looking for secure and scalable data handling solutions.
The study proposes an integrated compression-encryption system, a Python-based framework combining DEFLATE compression and AES-256 encryption. Theoretical foundations are based on hybrid algorithms and secure key management, and a unidirectional workflow ensures data integrity. The methodology includes benchmarking on metrics such as compression ratio, throughput and memory utilization, tested on high-performance hardware under controlled conditions.
The results demonstrate DEFLATE’s superiority in speed and AES-256’s cryptographic efficiency, achieving 99.9% compression ratios. LZMA excels in compression depth but demands excessive memory, limiting edge-device applicability. The key conclusions advocate DEFLATE + AES-256 for time-sensitive tasks and highlight metadata inflation as a critical bottleneck. Future work should explore hybrid pipelines and metadata-efficient formats to enhance usability and resource allocation. This research provides actionable recommendations for industries looking for secure and scalable data handling solutions.