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Evidence record 12983 · automatically gathered

End-to-End Differential Privacy in Training Deep Neural Network Classifiers

Differentially private machine learning enables model training on sensitive data while ensuring that individual data is unlikely to be recoverable from the parameters of the resulting model. However, existing work often privatizes both training inputs and their labels, and these protections may be conservative when labels are public or can be safely made public. Therefore, in this work we propose a novel private training framework that instead privatizes training inputs while keeping labels publ

Record details

Published: 21 July 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Privacy
Retrieved: 23 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (21 July 2026), “End-to-End Differential Privacy in Training Deep Neural Network Classifiers,” evidence record 12983, https://ethics.ai/record/12983 (originally published by arXiv cs.CR (AI security)).

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