conftrace_

differential privacy

1,207 papers

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Also known as

DP

Co-occurring keywords · papers mentioning both

federated learning (146) stochastic gradient descent (72) sample complexity (68) regret bound (53) privacy-preserving learning (49) privacy preservation (47) privacy-preserving machine learning (43) privacy-utility trade-off (42) synthetic data (37) large language model (36)

Papers

Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages ICML 2024 Private Zeroth-Order Nonsmooth Nonconvex Optimization ICLR 2024 PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models ACL 2024 PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds IJCAI 2024 Protect Your Score: Contact-Tracing with Differential Privacy Guarantees AAAI 2024 Provable Privacy with Non-Private Pre-Processing ICML 2024 Public-data Assisted Private Stochastic Optimization: Power and Limitations NeurIPS 2024 Pure Differential Privacy for Functional Summaries with a Laplace-like Process JMLR 2024 Revisiting Differentially Private ReLU Regression NeurIPS 2024 Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components AISTATS 2024 Sample-Efficient Private Learning of Mixtures of Gaussians NeurIPS 2024 Scalable DP-SGD: Shuffling vs. Poisson Subsampling NeurIPS 2024 SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures Interspeech 2024 Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients EMNLP 2024 Shifted Interpolation for Differential Privacy ICML 2024 Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes COLT 2024 Some Constructions of Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise COLT 2024 Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models NAACL 2024 Taming Heavy-Tailed Losses in Adversarial Bandits and the Best-of-Both-Worlds Setting NeurIPS 2024 Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence AISTATS 2024 Tangent Transformers for Composition,Privacy and Removal ICLR 2024 The Impact of Differential Privacy on Group Disparity Mitigation NAACL 2024 The importance of feature preprocessing for differentially private linear optimization ICLR 2024 The Limits of Differential Privacy in Online Learning NeurIPS 2024 The Privacy Power of Correlated Noise in Decentralized Learning ICML 2024