Performance Evaluation across Multiple Formats of Medical Data Compression and Security Using Blockchain Technology

This paper presents a comparative analysis of various medical data formats; TXT, PDF, DICOM, and JPG/PNG compressed and secured using a blockchain-integrated framework. The proposed system combines Zlib lossless compression, AES encryption, and blockchain hashing to ensure decentralized, tamper-proof, and efficient storage of electronic health records (EHRs). Implemented as a Flask-based Python application, performance was evaluated across multiple parameters, including compression ratio, encryption/decryption time, and blockchain hash latency. Results showed that TXT files achieved the highest compression ratio (~6.4), followed by PDF and image formats, while encryption and decryption times scaled linearly with file size. Blockchain hashing averaged between 5–13 ms, proving efficient for real-time healthcare environments. The comparative analysis reveals that text-based formats are most storage-efficient, while image formats (DICOM, PNG) require more computation but provide richer diagnostic data. The integration of blockchain ensures data integrity, traceability, and confidentiality, offering a reliable solution for medical data management. Future work will address blockchain scalability and explore the integration of biometric authentication for enhanced access control.