Differential privacy permits generation in the limit for any countable collection of languages but prohibits identification for collections with two languages having infinite intersection and finite difference; in stochastic settings, private identification is possible exactly when adversarial non-私
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14 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
First shuffle-DP and joint-DP algorithms for GLM contextual bandits achieve near non-private regret without strong spectral assumptions on contexts.
PrivCode++ introduces the first DP code generation method protecting both prompts and code via latent-conditioned two-stage training, claiming higher utility and stronger privacy than prior baselines.
TL++ recovers centralized mini-batch gradients via virtual batches in split learning and adds secret sharing for cut-layer tensors, achieving 91.41% accuracy on CIFAR-10 with 13x lower communication than full-model sync.
SMA-DP-SGD augments DP-SGD with a spectral memory-aware fractional branch from prior privatized updates to improve accuracy on CIFAR and MNIST while preserving conditional differential privacy.
FO-DP-SGD adds fractional-order memory to the private gradient release in DP-SGD, achieving better test accuracy on SVHN, CIFAR-10, and CIFAR-100 while using standard Rényi DP accounting with adjusted sensitivity βC.
CAPS provides an iterative differentially private synthesis method that outperforms one-shot baselines on authentic educational real-world data.
Shuffled DP-SGD requires σ ≥ 1/√(2 ln M) or κ ≥ (1/√8)(1 - 1/√(4π ln M)) to limit adversarial advantage, preventing strong privacy and high utility simultaneously.
Differential privacy reduces algorithmic collective action effectiveness, with formal lower bounds on success probability depending on collective size and privacy parameters, plus experimental verification on neural nets.
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
CA-ADP adjusts differential privacy noise per mini-batch class composition to improve F-scores by 3.3-8.5% over standard DP on three fall-detection datasets while claiming formal (ε,δ) guarantees.
citing papers explorer
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Differentially Private Language Generation and Identification in the Limit
Differential privacy permits generation in the limit for any countable collection of languages but prohibits identification for collections with two languages having infinite intersection and finite difference; in stochastic settings, private identification is possible exactly when adversarial non-私
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Shuffle and Joint Differential Privacy for Generalized Linear Contextual Bandits
First shuffle-DP and joint-DP algorithms for GLM contextual bandits achieve near non-private regret without strong spectral assumptions on contexts.
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PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees
PrivCode++ introduces the first DP code generation method protecting both prompts and code via latent-conditioned two-stage training, claiming higher utility and stronger privacy than prior baselines.
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TL++: Accuracy and Privacy Preserving Traversal Learning for Distributed Intelligent Systems
TL++ recovers centralized mini-batch gradients via virtual batches in split learning and adds secret sharing for cut-layer tensors, achieving 91.41% accuracy on CIFAR-10 with 13x lower communication than full-model sync.
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SMA-DP: Spectral Memory-Aware Differential Privacy for Deep Learning
SMA-DP-SGD augments DP-SGD with a spectral memory-aware fractional branch from prior privatized updates to improve accuracy on CIFAR and MNIST while preserving conditional differential privacy.
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Deep Learning under Fractional-Order Differential Privacy
FO-DP-SGD adds fractional-order memory to the private gradient release in DP-SGD, achieving better test accuracy on SVHN, CIFAR-10, and CIFAR-100 while using standard Rényi DP accounting with adjusted sensitivity βC.
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Cyclic Adaptive Private Synthesis for Sharing Real-World Data in Education
CAPS provides an iterative differentially private synthesis method that outperforms one-shot baselines on authentic educational real-world data.
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Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
Shuffled DP-SGD requires σ ≥ 1/√(2 ln M) or κ ≥ (1/√8)(1 - 1/√(4π ln M)) to limit adversarial advantage, preventing strong privacy and high utility simultaneously.
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Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy
Differential privacy reduces algorithmic collective action effectiveness, with formal lower bounds on success probability depending on collective size and privacy parameters, plus experimental verification on neural nets.
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Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
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Class-Aware Adaptive Differential Privacy in Deep Learning for Sensor-Based Fall Detection
CA-ADP adjusts differential privacy noise per mini-batch class composition to improve F-scores by 3.3-8.5% over standard DP on three fall-detection datasets while claiming formal (ε,δ) guarantees.
- Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
- PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization
- Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds