Deep Learning With Data Privacy via Residual Perturbation
Protecting data privacy in deep learning (DL) is of crucial importance. Several celebrated privacy notions have been established and used for privacy-preserving DL. However, many existing mechanisms achieve privacy at the cost of significant utility degradation and computational overhead. In this pa...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 48(2026), 3 vom: 09. Feb., Seite 3458-3470 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2026
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Online verfügbar |
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