A Comprehensive Methodology for Data Compression and Decompression Utilizing Huffman Coding, LZW Compression, and Run-Length Encoding, Integrated with Data Encryption Standard (DES) and Advanced Encryption Standard (AES) for Enhanced Security
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Abstract
With advancements in communication technologies, transitioning from 5G to 6G systems has led to exponential data growth, requiring secure and efficient data transmission solutions. This study integrates data compression techniques—Huffman Coding, Lempel-Ziv-Welch (LZW), and Run-Length Encoding (RLE)—with symmetric encryption algorithms, AES (Advanced Encryption Standard) and DES (Data Encryption Standard). The primary goal is to enhance computational performance while ensuring data security. Using a 32-byte dataset and implementing the algorithms in Go language via Visual Studio IDE, results demonstrate the significant reduction in encryption time when combining compression and encryption. Among the AES combinations, AES with Huffman Coding showed the highest efficiency, reducing encryption time by approximately 15% compared to standalone AES. Similarly, DES paired with LZW compression achieved a 20% improvement in computational time over standalone DES. The findings emphasize that selecting the optimal combination depends on data type and user requirements, facilitating secure and efficient communication in high-bandwidth, low-latency 6G systems. This research underscores the potential of cryptography, combined with compression, to enhance data transmission efficiency without compromising security. The integration approach highlights cryptographic strength in safeguarding big data, addressing challenges in modern technologies like the Internet of Everything (IoE). These results establish a foundation for future secure communication frameworks, promoting reliable and scalable cryptographic solutions tailored for 6G and beyond
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J. D. A. Correa, A. S. R. Pinto, and C. Montez, "Lossy Data Compression for IoT Sensors: A Review," Internet of Things, vol. 19, p. 100516, 2022. https://doi.org/10.1016/j.iot.2022.100516
R. Bhanot and R. Hans, "A review and comparative analysis of various encryption algorithms," International Journal of Security Its Applications, vol. 9, no. 4, pp. 289-306, 2015. https://www.earticle.net/Article/A245530
A. Moffat, "Huffman coding," ACM Computing Surveys, vol. 52, no. 4, pp. 1-35, 2019. https://doi.org/10.1145/3342555
H. Dheemanth, "LZW data compression," American Journal of Engineering Research, vol. 3, no. 2, pp. 22-26, 2014. http://www.ajer.org/
B. Strasser, A. Botea, and D. Harabor, "Compressing optimal paths with run length encoding," Journal of Artificial Intelligence Research, vol. 54, pp. 593-629, 2015. https://doi.org/10.1613/jair.4931
K.-L. Tsai, Y.-L. Huang, F.-Y. Leu, I. You, Y.-L. Huang, and C.-H. Tsai, "AES-128 based secure low power communication for LoRaWAN IoT environments," Ieee Access, vol. 6, pp. 45325-45334, 2018. DOI: https://doi.org/10.1109/ACCESS.2018.2852563
K. Logunleko, O. Adeniji, and A. Logunleko, "A comparative study of symmetric cryptography mechanism on DES AES and EB64 for information security," Int. J. Sci. Res. in Computer Science Engineering, vol. 8, no. 1, 2020. https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=1690
B. Carpentieri, "Efficient compression and encryption for digital data transmission," Security Communication Networks, vol. 2018, 2018. https://doi.org/10.1155/2018/9591768
X.-J. Tong, P. Chen, and M. Zhang, "A joint image lossless compression and encryption method based on chaotic map," Multimedia Tools Applications, vol. 76, no. 12, pp. 13995-14020, 2017. https://doi.org/10.1007/s11042-016-3775-6
A. Anand, A. K. Singh, Z. Lv, and G. Bhatnagar, "Compression-then-encryption-based secure watermarking technique for smart healthcare system," IEEE MultiMedia, vol. 27, no. 4, pp. 133- 143, 2020. DOI: https://doi.org/10.1109/MMUL.2020.2993269
M. E. Hameed, M. M. Ibrahim, N. Abd Manap, and A. A. Mohammed, "A lossless compression and encryption mechanism for remote monitoring of ECG data using Huffman coding and CBC-AES," Future generation computer systems, vol. 111, pp. 829-840, 2020. https://doi.org/10.1016/j.future.2019.10.010
M. R. Ashila, N. Atikah, E. H. Rachmawanto, and C. A. Sari, "Hybrid AES-Huffman Coding for Secure Lossless Transmission," in 2019 Fourth International Conference on Informatics and Computing (ICIC), 2019, pp. 1-5: IEEE. DOI: https://doi.org/10.1109/ICIC47613.2019.8985899
P. S. Mukesh, M. S. Pandya, and S. Pathak, "Enhancing AES algorithm with arithmetic coding," in 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), 2013, pp. 83-86: IEEE. DOI: https://doi.org/10.1109/ICGCE.2013.6823404
M. Kumari, V. Pawar, P. J. I. J. o. N. S. Kumar, and I. A. Vol, "A novel image encryption scheme with Huffman encoding and steganography technique," International Journal of Network Security Its Applications, vol. 11, 2019 2019. https://ssrn.com/abstract=3847524
A. K. Joshi and S. Sharma, "Reversible data hiding by utilizing AES encryption and LZW compression," in Proceedings of International Conference on Recent Advancement on Computer and Communication, 2018, pp. 73-81: Springer. https://doi.org/10.1007/978-981-10-8198-9_8
T. Yue, C. Wang, and Z.-x. Zhu, "Hybrid encryption algorithm based on wireless sensor networks," in 2019 IEEE international conference on mechatronics and automation (ICMA), 2019, pp. 690- 694: IEEE. DOI: https://doi.org/10.1109/ICMA.2019.8816451
Sisodia, Mr. A., Mrs. Swati, & Hashmi, Mrs. H. (2020). Incorporation of Non-Fictional Applications in Wireless Sensor Networks. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 11, pp. 42–49). https://doi.org/10.35940/ijitee.k7673.0991120
Patil, Mrs. Suvarna. S., & Vidyavathi, Dr. B. M. (2022). Application o f Advanced Machine Learning and Artificial Neural Network Methods in Wireless Sensor Networks Based Applications. In International Journal of Engineering and Advanced Technology (Vol. 11, Issue 3, pp. 103–109). https://doi.org/10.35940/ijeat.c3394.0211322
Sharma, P. (2023). Zigbee based Wireless Sensor Network for Smart Energy Meter. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 12, Issue 3, pp. 20–27). https://doi.org/10.35940/ijrte.c7861.0912323
Chitransh, A., & Kalyan, B. S. (2021). ARM Microcontroller Based Wireless Industrial Automation System. In Indian Journal of Microprocessors and Microcontroller (Vol. 1, Issue 2, pp. 8–11). https://doi.org/10.54105/ijmm.b1705.091221
Pramod, K., Mrs. Durga, M., Apurba, S., & Shashank, S. (2023). An Efficient LEACH Clustering Protocol to Enhance the QoS of WSN. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 3, Issue 3, pp. 1–8). https://doi.org/10.54105/ijainn.a3822.043323