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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
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| Paper Title: | Privacy-Preserving Machine Learning using Deidentification Techniques for HR and Payroll Data |
| Authors Name: | Shanmugaraja Krishnasamy Venugopal |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_207372
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| Published Paper Id: | IJRTI2511067 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i11.207372 |
| Abstract: | The use of machine learning in Human Resources (HR) and payroll systems represents significant capability gains for automation, decision-making, and operational efficiencies. However, this transition process raises significant concerns related to the protection of sensitive employee data. Privacy-preserving computer scientific methods, like de-identification (removing or altering identifiers such that the individual cannot be identified), are becoming an increasingly important way to preserve privacy while maintaining the richness of datasets for use in analytical research. The purpose of this review article is to provide a comprehensive report on various privacy-preserving techniques, especially de-identification techniques common to HR and payroll data. The review will include real-world examples of privacy-preserving methods, including CV de-identification, clustering of quasi-identifiers (QI), narrative-level de-identification, and federated learning (model training without the data leaving the local source, but sharing model updates). The article looks at implications for privacy and utility trade-offs, assesses potential reidentification risks, and summarizes innovations in secured data, such as format-preserving transformations (anonymizing and preserving values such as IDs/dates) and voice anonymization. The review article considers issues identified in the current literature, as well as policy implications and future directions for utilizing secure and compliant machine learning frameworks in human resource settings. |
| Keywords: | Privacy-preserving machine learning, Deidentification techniques, HR data privacy, Payroll anonymization |
| Cite Article: | "Privacy-Preserving Machine Learning using Deidentification Techniques for HR and Payroll Data", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a562-a568, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511067.pdf |
| Downloads: | 000277 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2511067
Registration ID:207372
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i11.207372
Page No: a562-a568 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511067 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511067 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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