Abstract
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.
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References
Cui, B., Zhang, B., Wang, K.: A data masking scheme for sensitive big data based on format-preserving encryption. In: 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), vol. 1, pp. 518–524. IEEE (2017)
Khanam, T., Siuly, S., Wang, H.: Analysing big brain signal data for advanced brain computer interface system. In: Hua, W., Wang, H., Li, L. (eds.) ADC 2022. LNCS, vol. 13459, pp. 103–114. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-15512-3_8
Khanam, T., Siuly, S., Wang, H.: An optimized artificial intelligence based technique for identifying motor imagery from EEGs for advanced brain computer interface technology. Neural Comput. Appl. 35(9), 6623–6634 (2023)
Siuly, S., Li, Y., Zhang, Y.: EEG signal analysis and classification. IEEE Trans. Neural Syst. Rehabilit. Eng. 11, 141–144 (2016)
Siuly, S., Li, Y.: Discriminating the brain activities for brain-computer interface applications through the optimal allocation-based approach. Neural Comput. Appl. 26, 799–811 (2015)
Alvi, A.M., Siuly, S., Wang, H.: Neurological abnormality detection from electroencephalography data: a review. Artif. Intell. Rev. 55(3), 2275–2312 (2022)
Farsi, L., Siuly, S., Kabir, E., Wang, H.: Classification of alcoholic EEG signals using a deep learning method. IEEE Sens. J. 21, 3552–3560 (2020)
Siuly, S., et al.: A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 28(9), 1966–1976 (2020)
Tawhid, M.N.A., Siuly, S., Li, T.: A convolutional long short-term memory-based neural network for epilepsy detection from EEG. IEEE Trans. Instrum. Meas. 71, 1–11 (2022)
Tawhid, M.N.A., Siuly, S., Wang, K., Wang, H.: Automatic and efficient framework for identifying multiple neurological disorders from EEG signals. IEEE Trans. Technol. Soc. 4(1), 76–86 (2023)
Siuly, S., Li, Y., Zhang, Y.: Significance of EEG signals in medical and health research. In: EEG Signal Analysis and Classification. HIS, pp. 23–41. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47653-7_2
Martinovic, I., Davies, D., Frank, M., Perito, D., Ros, T., Song, D.: On the feasibility of \(\{\)Side-Channel\(\}\) attacks with \(\{\)Brain-Computer\(\}\) interfaces. In: 21st USENIX Security Symposium (USENIX Security 2012), pp. 143–158 (2012)
Mandal, A., Saxena, N.: SoK: your mind tells a lot about you: on the privacy leakage via brainwave devices. In: Proceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks, pp. 175–187 (2022)
Sun, X., Wang, H., Li, J., Pei, J.: Publishing anonymous survey rating data. Data Min. Knowl. Discov. 23, 379–406 (2011)
Yin, J., Tang, M., Cao, J., Wang, H., You, M., Lin, Y.: Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web (2022)
Kabir, E., Wang, H.: Conditional purpose based access control model for privacy protection. In: Proceedings of the Twentieth Australasian Conference on Australasian Database, vol. 92, pp. 137–144 (2009)
Ge, Y.-F., Orlowska, M., Cao, J., Wang, H., Zhang, Y.: MDDE: multitasking distributed differential evolution for privacy-preserving database fragmentation. VLDB J. 31, 1–19 (2022)
Popescu, A.B., et al.: Privacy preserving classification of EEG data using machine learning and homomorphic encryption. Appl. Sci. 11(16), 7360 (2021)
Agarwal, A., et al.: Protecting privacy of users in brain-computer interface applications. IEEE Trans. Neural Syst. Rehabil. Eng. 27(8), 1546–1555 (2019)
Hang, W., et al.: Fedeeg: federated EEG decoding via inter-subject structure matching. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE (2023)
Zhang, W., Wu, D.: Lightweight source-free transfer for privacy-preserving motor imagery classification. IEEE Trans. Cogn. Dev. Syst. 15(2), 938–949 (2022)
Debie, E., Moustafa, N., Whitty, M.T.: A privacy-preserving generative adversarial network method for securing EEG brain signals. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2020)
Nakachi, T., Ishihara, H., Kiya, H.: Privacy-preserving network BMI decoding of covert spatial attention. In: 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1–8. IEEE (2018)
Konduru, S.S., Saraswat, V.: Privacy preserving records sharing using blockchain and format preserving encryption. Cryptology ePrint Archive (2023)
Pérez-Resa, A., Garcia-Bosque, M., Sánchez-Azqueta, C., Celma, S.: A new method for format preserving encryption in high-data rate communications. IEEE Access 8, 21003–21016 (2020)
Karopoulos, G., Ntantogian, C., Xenakis, C.: Masker: masking for privacy-preserving aggregation in the smart grid ecosystem. Comput. Secur. 73, 307–325 (2018)
Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H.: Filter bank common spatial pattern algorithm on BCI competition iv datasets 2A and 2B. Front. Neurosci. 6, 39 (2012)
Frikha, T., Chaari, A., Chaabane, F., Cheikhrouhou, O., Zaguia, A.: [retracted] healthcare and fitness data management using the IoT-based blockchain platform. J. Healthc. Eng. 2021(1), 9978863 (2021)
Hernández-Álvarez, L., De Fuentes, J.M., González-Manzano, L., Encinas, L.H.: Smartcampp-smartphone-based continuous authentication leveraging motion sensors with privacy preservation. Pattern Recogn. Lett. 147, 189–196 (2021)
Vapnik, V.N.: The support vector method. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 261–271. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0020166
Huang, B.F., Boutros, P.C.: The parameter sensitivity of random forests. BMC Bioinform. 17, 1–13 (2016)
Murphy, K.P., et al.: Naive Bayes classifiers. Univ. Br. Columbia 18(60), 1–8 (2006)
Wu, H., et al.: Online privacy-preserving EEG classification by source-free transfer learning. IEEE Trans. Neural Syst. Rehabil. Eng. (2024)
Ge, Y.-F., Wang, H., Cao, J., Zhang, Y., Jiang, X.: Privacy-preserving data publishing: an information-driven distributed genetic algorithm. World Wide Web 27, 01 (2024)
Zhang, Y., Shen, Y., Wang, H., Yong, J., Jiang, X.: On secure wireless communications for IoT under eavesdropper collusion. IEEE Trans. Autom. Sci. Eng. 13(3), 1281–1293 (2016)
You, M., Ge, Y.-F., Wang, K., Wang, H., Cao, J., Kambourakis, G.: Hierarchical adaptive evolution framework for privacy-preserving data publishing. World Wide Web 27, 07 (2024)
Mongardi, S., Pinoli, P.: Exploring federated learning for emotion recognition on brain-computer interfaces. In: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, pp. 622–626 (2024)
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Khanam, T., Siuly, S., Wang, K., Zheng, Z. (2025). A Privacy-Preserving Encryption Framework for Big Data Analysis. In: Barhamgi, M., Wang, H., Wang, X. (eds) Web Information Systems Engineering – WISE 2024. WISE 2024. Lecture Notes in Computer Science, vol 15440. Springer, Singapore. https://doi.org/10.1007/978-981-96-0576-7_7
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