Publication2020
A Comparison Study of Different Privacy Preserving Techniques in Collaborative Filtering Based Recommender System
The record
- Type
- Publication
- Date
- 1 January 2020
- Level
- National
- Journal
- Advances in Intelligent Systems and Computing
- Volume
- 990
- Pages
- 193-203
- Published
- 1 January 2020
- DOI
- 10.1007/978-981-13-8676-3_17
- Scopus ID
- 85077118174
- ISSN
- 21945357
- Authors
- 2
- Open access
- No
- Citations
- 5
- 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 collaborative filtering addresses data protection and institutional accountability in recommendation systems.
- 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
Privacy-preserving machine learning algorithms represent research innovation in cybersecurity.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-4.1 Transparency, accountability and the rule of law
Privacy protection in digital systems supports transparency and accountability in data management.
- 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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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “A Comparison Study of Different Privacy Preserving Techniques in Collaborative Filtering Based Recommender System”. Kortex.