A Privacy-Preserving Encryption Framework for Big Data Analysis

The advent of big data has brought numerous conveniences and benefits but has also heightened users’ privacy concerns. Traditional methods like data masking and encryption secure user access control but suffer from storage space wastage due to data padding limitations. Moreover, these systems face decoding challenges and risk exposing confidential information after decryption. To overcome these issues, this study aims to develop a format-preserving encryption (FPE) based privacy-preserving technique to maintain user access control while optimizing anomaly detection accuracy and minimizing information loss. This method first generates a fixed-length key for each algorithm based on specified key length parameters, then continue the same length and format for the ciphertext as the original plaintext ensuring compatibility with databases. Our analysis of accuracy, information loss over ac-curacy, and information loss over root mean square error (RMSE) demonstrates the overall efficacy of the proposed method. Our experiment on brain computer interface (BCI) based electroencephalogram (EEG) data achieves 96.55% accuracy and requires only 2.41 s of computation for user access control. Remarkably, use of cryptography does not significantly impact performance compared to a non-privacy-preserving framework. Our developed framework will guide future researchers to develop more effective privacy protection mechanisms in BCI technology, ensuring the security of confidential information.