| Author Name | Affiliation | Postcode | | Lavanya M* | Department of Computer Science, Vels Institute of Science Technology & Advanced Studies (VISTAS), Pallavaram, Chennai 600117, Tamilnadu, India | 600117 | | Mangayarkarasi S | Department of Computer Science, Vels Institute of Science Technology & Advanced Studies (VISTAS), Pallavaram, Chennai 600117, Tamilnadu, India | 600117 |
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| Abstract: |
| Big data has ushered in an era of unprecedented access to vast amounts of new, unstructured data, particularly in the realm of sensitive information. This data presents unique opportunities for enhancing risk alerting systems, but also poses challenges in terms of extraction and analysis due to its diverse file formats. This paper proposes the utilization of a DAE-based (Deep Auto Encoders) model for projecting risk associated with financial data. The research delves into the development of an indicator assessing the degree to which organizations successfully avoid displaying bias in handling financial information. Results from simulation experiments demonstrate the superior performance of the DAE algorithm, showcasing fewer false positives, improved overall detection rates, and a noteworthy 9% reduction in failure jitter. The optimized DAE algorithm achieves an accuracy of 99%, surpassing existing methods, thereby presenting a robust solution for sensitive data risk projection. |
| Key words: data mining sensitive data DAE |
| DOI：10.11916/j.issn.1005-9113.2024018 |
| Clc Number:TP39, F832.59 |
| Fund: |
