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
Rényi differential privacy
14 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 14representative 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.
ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
PACZero achieves zero mutual information privacy in LLM fine-tuning via sign-quantized subset-aggregated ZO gradients, delivering near non-private accuracy on SST-2 at I=0.
Derives tight closed-form bounds on f-DP trade-off functions for DP-SGD with random shuffling subsampling in the high-noise regime σ ≥ √(3/ln M), plus an asymptotic convergence result to the random guessing diagonal with O(√E) δ dependence.
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.
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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.