Privacy-preserving big data analytics for cyber-physical systems
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Cyber-physical systems (CPS) generate big data collected from combining physical and digital entities, but the challenge of CPS privacy-preservation demands further research to protect CPS sensitive information from unauthorized access. Data mining, perturbation, transformation and encryption are techniques extensively used to preserve private information from disclosure whilst still providing insight, but these are limited in their effectiveness in still allowing high-level analysis. This paper studies the role of big data component analysis for protecting sensitive information from illegal access. The independent component analysis (ICA) technique is applied to transform raw CPS information into a new shape whilst preserving its data utility. The mechanism is evaluated using the power CPS dataset, and the results reveal that the technique is more effective than four other privacy-preservation techniques, obtaining a higher level of privacy protection. In addition, the data utility is tested using three machine learning algorithms to estimate their capability of identifying normal and attack patterns before and after transformation.
KeywordsPrivacy preservation Big data Independent component analysis SCADA CPS Power system
We would like to thank the School of Engineering and Information Technology (SEIT) at UNSW@ADFA for sponsoring this work under the Cyber Physical Security project-PS47084.
- 3.Zakerzadeh, H., Aggarwal, C. C., & Barker, K. (2015). Privacy-preserving big data publishing. In Proceedings of the 27th international conference on scientific and statistical database management (p. 26). ACM.Google Scholar
- 5.Keshk, M., Moustafa, N., Sitnikova, E., & Creech, G. (2017). Privacy preservation intrusion detection technique for scada systems. arXiv preprint arXiv:1711.02828.
- 8.Power systems datasets. 2017. Available: https://sites.google.com/a/uah.edu/tommy-morris-uah/ics-data-sets. Accessed 10 Mar 2017.
- 10.Aggarwal, C. C., & Philip, S. Y. (2008). A general survey of privacy-preserving data mining models and algorithms. In Privacy-preserving data mining (pp. 11–52). Springer.Google Scholar
- 12.Fang, W., Zamani, M., & Chen, Z. (2018). Secure and privacy preserving consensus for second-order systems based on paillier encryption. arXiv preprint arXiv:1805.01065.
- 14.Femandes, M., & Gomes, J. (2017). Heuristic approach for association rule hiding using ECLAT. In 2017 2nd International conference on communication systems, computing and IT applications (CSCITA) (pp. 218–223). IEEE.Google Scholar
- 19.Ferrag, M. A., Maglaras, L. A., Janicke, H., & Jiang, J. (2016). A survey on privacy-preserving schemes for smart grid communications. arXiv preprint arXiv:1611.07722.
- 20.Cheung, J. C., Chim, T. W., Yiu, S.-M., Li, V. O., & Hui, L. C. (2011). Credential-based privacy-preserving power request scheme for smart grid network. In 2011 IEEE global telecommunications conference (GLOBECOM 2011) (pp. 1–5). IEEE.Google Scholar
- 21.Moustafa, N., Creech, G., & Slay, J. (2017). Big data analytics for intrusion detection system: Statistical decision-making using finite dirichlet mixture models. In Data analytics and decision support for cybersecurity (pp. 127–156). Springer.Google Scholar
- 24.Hink, R. C. B., Beaver, J. M., Buckner, M. A., Morris, T., Adhikari, U., & Pan, S. (2014). Machine learning for power system disturbance and cyber-attack discrimination. In 2014 7th international symposium on resilient control systems (ISRCS) (pp. 1–8). IEEE.Google Scholar