Pages that link to "Item:Q3593113"
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The following pages link to Our Data, Ourselves: Privacy Via Distributed Noise Generation (Q3593113):
Displaying 16 items.
- Secure sampling with sublinear communication (Q6114277) (← links)
- Privacy amplification for wireless federated learning with Rényi differential privacy and subsampling (Q6153177) (← links)
- Comparing approximate and probabilistic differential privacy parameters (Q6161437) (← links)
- Differential privacy: getting more for less (Q6198644) (← links)
- Helmholtz machine with differential privacy (Q6205016) (← links)
- Stochastic privacy-preserving methods for nonconvex sparse learning (Q6496176) (← links)
- Who started this rumor? Quantifying the natural differential privacy of gossip protocols (Q6535005) (← links)
- Differentially private federated learning with Laplacian smoothing (Q6567397) (← links)
- Selective privacy-preserving framework for large language models fine-tuning (Q6571191) (← links)
- Gaussian differentially private robust mean estimation and inference (Q6589584) (← links)
- General inferential limits under differential and pufferfish privacy (Q6596692) (← links)
- Adversarial machine learning for cybersecurity and computer vision: current developments and challenges (Q6601101) (← links)
- The promise and limitations of formal privacy (Q6602035) (← links)
- A framework for adversarial streaming via differential privacy and difference estimators (Q6623590) (← links)
- Certifying private probabilistic mechanisms (Q6652993) (← links)
- Compressing unit-vector correlations via sparse pseudorandom generators (Q6653033) (← links)
