Publication2021
A Machine Learning Approach in Data Perturbation for Privacy-Preserving Data Mining
The record
- Type
- Publication
- Date
- 1 January 2021
- Level
- National
- Journal
- Smart Innovation Systems and Technologies
- Volume
- 224
- Pages
- 645-654
- Published
- 1 January 2021
- DOI
- 10.1007/978-981-16-1502-3_64
- Scopus ID
- 85112725056
- ISSN
- 21903018
- Authors
- 2
- Open access
- No
- Citations
- 1
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
16.6 Develop effective, accountable and transparent institutions at all levels
Privacy-preserving data perturbation using machine learning protects confidential information through noise addition and regression models for secure data mining.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Research advances data protection methodology development for privacy-preserving analytical techniques.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-4.1 Transparency, accountability and the rule of law
Data protection research supports institutional accountability and privacy governance in information security.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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KIIT Deemed to be University. “A Machine Learning Approach in Data Perturbation for Privacy-Preserving Data Mining”. Kortex.