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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Lavanya M,Mangayarkarasi S.A Deep Auto-encoder Based Security Mechanism for Protecting Sensitive Data Using AI Based Risk Assessment[J].Journal of Harbin Institute Of Technology(New Series),2025,32(4):90-98.DOI:10.11916/j.issn.1005-9113.2024018.
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A Deep Auto-encoder Based Security Mechanism for Protecting Sensitive Data Using AI Based Risk Assessment
Author NameAffiliation
Lavanya M Department of Computer Science, Vels Institute of Science Technology & Advanced Studies VISTAS, Chennai600117, India 
Mangayarkarasi S Department of Computer Science, Vels Institute of Science Technology & Advanced Studies VISTAS, Chennai600117, India 
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. It 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. Simulation results 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  deep auto-encoders
DOI：10.11916/j.issn.1005-9113.2024018
Clc Number:TP39,F832.59
Fund:

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