arXiv (All)
open access
Label Differential Privacy via Aggregation
This paper explores the use of linear aggregation to protect the privacy of sensitive training labels through the concept of \emph{label differential privacy} (label-DP) while maintaining regression task utility. Our key finding is that weighted linear aggregation of training instances with i.i.d. $
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Privacy Leakage via Output Label Space and Differentially Private Continual Learning#163068
- TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning#341905
- Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant#144165
- Efficient Verifiable Protocol for Privacy-Preserving Aggregation in Federated Learning#177285
- Keeping Privacy Labels Honest#400464
Record · ID 997900
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive