Abstract

This study focuses on the aspects of securing financial transaction data through encryption and other forms of encryption. The most recommended ones are AES-256-GCM encryption and its application towards having data integrity and confidentiality at the time of transmission. The study describes the threat involved in the transfer and storage concerning financial data. Combining with secure transport protocols shows the clear way in which AES-256-GCM encryption would protect other types of sensitive data, such as through IPsec VPN tunnels. Both management services and the market are dependent on data technologies, since new forms of services and facilities are being developed with each single device. Some of the challenges associated with this area were given as performance overhead due to encryption, and the system's inability to scale to accommodate large amounts of financial data. However, with this method, it has taken care of the industry-standard compliance aspects, making it, thereby, a formidable ground in the security of financial transactions in the digital environment. The results obtained from As shown in the graph, the increase in plaintext size (P_size) correlates with the increase in AES-256-GCM encryption time (T_encryption), which goes from about 20 ms for 1 MB to over 120 ms for 20 MB, indicating that more and more computational work is required to encrypt such a large data file. In the meantime, the security (S_encryption) of the encrypted data-which is graphically represented by the blue line-increases immensely when the Data size becomes larger. On the contrary, the red line, which refers to that of unencrypted data, remains static to illustrate the low security level. Thus, encryption becomes necessary to secure sensitive data as it grows in size.

Keywords

  • synthetic estimator
  • quadratic product estimator
  • ratio cum product estimator

References

  1. 1. Wang, Y., Tao, X., Ni, J., & Yu, Y. (2018). Data integrity checking with reliable data transfer for secure cloud storage. International Journal of Web and Grid Services, 14(1), 106-121.
  2. 2. Zhang, Y., Xu, C., Liang, X., Li, H., Mu, Y., & Zhang, X. (2016). Efficient public verification of data integrity for cloud storage systems from indistinguishability obfuscation. IEEE Transactions on Information Forensics and Security, 12(3), 676-688.
  3. 3. Shen, W., Qin, J., Yu, J., Hao, R., & Hu, J. (2018). Enabling identity-based integrity auditing and data sharing with sensitive information hiding for secure cloud storage. IEEE Transactions on Information Forensics and Security, 14(2), 331-346.
  4. 4. Liang, W., Tang, M., Long, J., Peng, X., Xu, J., & Li, K. C. (2019). A secure fabric blockchain-based data transmission technique for industrial Internet-of-Things. IEEE Transactions on Industrial Informatics, 15(6), 3582-3592.
  5. 5. Wang, T., Bhuiyan, M. Z. A., Wang, G., Qi, L., Wu, J., & Hayajneh, T. (2019). Preserving balance between privacy and data integrity in edge-assisted Internet of Things. IEEE Internet of Things Journal, 7(4), 2679-2689.
  6. 6. Yu, Y., Au, M. H., Ateniese, G., Huang, X., Susilo, W., Dai, Y., & Min, G. (2016). Identity-based remote data integrity checking with perfect data privacy preserving for cloud storage. IEEE Transactions on Information Forensics and Security, 12(4), 767-778.
  7. 7. Tan, S., Song, W. Z., Stewart, M., Yang, J., & Tong, L. (2016). Online data integrity attacks against real-time electrical market in smart grid. IEEE transactions on smart grid, 9(1), 313-322.
  8. 8. Elhoseny, M., & Shankar, K. (2019). Reliable data transmission model for mobile ad hoc network using signcryption technique. IEEE transactions on reliability, 69(3), 1077-1086.
  9. 9. Yuan, J., & Yu, S. (2015). Public integrity auditing for dynamic data sharing with multiuser modification. IEEE Transactions on Information Forensics and Security, 10(8), 1717-1726.
  10. 10. Xu, Y., Ren, J., Zhang, Y., Zhang, C., Shen, B., & Zhang, Y. (2019). Blockchain empowered arbitrable data auditing scheme for network storage as a service. IEEE Transactions on Services Computing, 13(2), 289-300.
  11. 11. Li, Y., Yu, Y., Min, G., Susilo, W., Ni, J., & Choo, K. K. R. (2017). Fuzzy identity-based data integrity auditing for reliable cloud storage systems. IEEE Transactions on Dependable and Secure Computing, 16(1), 72-83.
  12. 12. Zhang, A., Wang, L., Ye, X., & Lin, X. (2016). Light-weight and robust security-aware D2D-assist data transmission protocol for mobile-health systems. IEEE Transactions on Information Forensics and Security, 12(3), 662-675.
  13. 13. Yang, Q., An, D., Min, R., Yu, W., Yang, X., & Zhao, W. (2017). On optimal PMU placement-based defense against data integrity attacks in smart grid. IEEE Transactions on Information Forensics and Security, 12(7), 1735-1750.
  14. 14. Wang, B., Kong, W., & Li, W. (2019). A Dual-Chaining Watermark Scheme for Data Integrity Protection in Internet of Things. Computers, Materials & Continua, 58(3).
  15. 15. Zhang, A., Chen, J., Hu, R. Q., & Qian, Y. (2015). SeDS: Secure data sharing strategy for D2D communication in LTE-Advanced networks. IEEE Transactions on Vehicular Technology, 65(4), 2659-2672.
  16. 16. Li, J., Yan, H., & Zhang, Y. (2018). Certificateless public integrity checking of group shared data on cloud storage. IEEE Transactions on Services Computing, 14(1), 71-81.
  17. 17. Hang, L., & Kim, D. H. (2019). Design and implementation of an integrated iot blockchain platform for sensing data integrity. sensors, 19(10), 2228.
  18. 18. Jiang, T., Chen, X., & Ma, J. (2015). Public integrity auditing for shared dynamic cloud data with group user revocation. IEEE Transactions on Computers, 65(8), 2363-2373.
  19. 19. Wang, H., He, D., & Tang, S. (2016). Identity-based proxy-oriented data uploading and remote data integrity checking in public cloud. IEEE Transactions on Information Forensics and Security, 11(6), 1165-1176.
  20. 20. He, D., Kumar, N., Zeadally, S., Vinel, A., & Yang, L. T. (2017). Efficient and privacy-preserving data aggregation scheme for smart grid against internal adversaries. IEEE Transactions on Smart Grid, 8(5), 2411-2419.
  21. 21. Ahmed, S., Lee, Y., Hyun, S. H., & Koo, I. (2019). Unsupervised machine learning-based detection of covert data integrity assault in smart grid networks utilizing isolation forest. IEEE Transactions on Information Forensics and Security, 14(10), 2765-2777.
  22. 22. Partala, J. (2018). Provably secure covert communication on blockchain. Cryptography, 2(3), 18.
  23. 23. Xu, X., Zhang, X., Gao, H., Xue, Y., Qi, L., & Dou, W. (2019). BeCome: Blockchain-enabled computation offloading for IoT in mobile edge computing. IEEE Transactions on Industrial Informatics, 16(6), 4187-4195.
  24. 24. Zhang, Y., Xu, C., Lin, X., & Shen, X. (2019). Blockchain-based public integrity verification for cloud storage against procrastinating auditors. IEEE Transactions on Cloud Computing, 9(3), 923-937.
  25. 25. Tao, H., Bhuiyan, M. Z. A., Abdalla, A. N., Hassan, M. M., Zain, J. M., & Hayajneh, T. (2018). Secured data collection with hardware-based ciphers for IoT-based healthcare. IEEE Internet of Things Journal, 6(1), 410-420.