Privacy-Preserving Synthetic Banking Data Generation Using Generative  Adversarial Networks

Adarsh Naidu

Privacy-Preserving Synthetic Banking Data Generation Using Generative Adversarial Networks

Authors

Adarsh Naidu

Keywords:

Synthetic Data Generation, Privacy Preservation, Generative Adversarial Networks, Banking Data, Financial Privacy, Data Security, Machine Learning, Deep Learning

Abstract

The rapid digital transformation of the banking and financial services sector has led to the largescale collection and utilization of customer transaction data for tasks such as fraud detection, credit risk assessment, customer behavior modeling

References

L. Sweeney, “k-anonymity: A model for protecting privacy,” Int. J. Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 5, pp. 557–570, 2002.

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Published

2024-02-20

How to Cite

Adarsh Naidu. (2024). Privacy-Preserving Synthetic Banking Data Generation Using Generative Adversarial Networks . Journal of Computational Analysis and Applications (JoCAAA), 32(2), 787–798. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5177