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Balancing Data Utility and Data Privacy using Synthetic Data for Cyber Physical Systems

Manas Kumar Yogi

Abstract


Cyber-physical systems are becoming famous and indispensable part of smart environment. As we say so, the need for preserving the trust of users is also multiplying with time. The CPS should be designed in such a way that the private and sensitive data of users in a CPS ecosystem needs to be protected without effecting the utility of data. This issue leads to a trade-off between the two aspects which can be balanced by the introduction of conceptual framework of synthetic data and its properties. The essential features of synthetic data are projected in a concise manner in this study which will help the engineers working in this research domain to a certain degree of usefulness.

Keywords


Anonymization, cyber physical systems, synthetic, utility, privacy

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References


Elliot M, O’hara K, Raab C, O’Keefe CM, Mackey E, Dibben C, Gowans H, Purdam K, McCullagh K. Functional anonymisation: Personal data and the data environment. Comput Law Secur Rev. 2018; 34(2): 204–221.

Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M. L-diversity: privacy beyond k-anonymity. In 22nd International Conference on Data Engineering (ICDE’06). 2006; 24–24.

Chen D, Yu N, Zhang Y, Fritz M. GAN-Leaks: A Taxonomy of Membership Inference Attacks against GANs. arXiv:1909.03935 [cs]. 2019 Sep.

Hayes J, Melis L, Danezis G, Cristofaro ED. LOGAN: Membership Inference Attacks Against Generative Models. Proceedings on Privacy Enhancing Technologies. 2019 Jan; (1): 133–152.

Hilprecht B, H¨arterich M, Bernau D. Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models. Proceedings on Privacy Enhancing Technologies. 2019 Oct; (4): 232–249.

Abowd JM, Vilhuber L. How protective are synthetic data? In International Conference on Privacy in Statistical Databases; Springer; 2008; 239–246.

Bindschaedler V, Shokri R, Gunter CA. Plausible deniability for privacy-preserving data synthesis. arXiv preprint arXiv: 1708.07975. 2017.

Hazy. (2019). Safe synthetic data - privacy, utility and control. https://hazy. com/images/videos/hazy-privacy-explainer.pdf. Accessed 2020-11-12.

Koller D, Friedman N. Probabilistic graphical models: principles and techniques. MIT press; 2009.

Ghahramani Z. An introduction to hidden Markov models and Bayesian networks. In Hidden Markov models: applications in computer vision. World Scientific. 2001; 9–41.

Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y. Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS). 2014; 2: 2672–2680.


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