Privacy

Efficiently and privately reconstructing marginal queries from noisy data using residuals improves accuracy of existing differential privacy mechanisms.

DOPPLER, a novel low-pass filter, significantly enhances differentially private (DP) optimizer performance by reducing the impact of privacy noise, bridging the gap between DP and non-DP training.

Dimension-free private mean estimation is achieved for anisotropic data, breaking the curse of dimensionality in privacy-preserving high-dimensional analysis.

Tighter differential privacy (RDP) guarantees for DP-SGD with fixed-size minibatches are achieved, improving private deep learning model training.

Differentially private set representations achieve optimal privacy-utility tradeoffs with exponentially smaller error than prior histogram methods.

This paper presents DP-Nash-VI, a novel algorithm ensuring trajectory-wise privacy in multi-agent reinforcement learning, achieving near-optimal regret bounds under both joint and local differential p…

This paper presents new, nearly optimal differentially private algorithms for handling sparse gradients, significantly improving efficiency and scalability in large embedding models.

This paper introduces a novel differentially private graph diffusion framework ensuring edge-level privacy, significantly improving utility-privacy trade-offs for personalized PageRank computation.

First differentially private algorithm for testing equivalence between continuous distributions, enabling privacy-preserving comparisons of sensitive data.

Differentially private scalable kernel learning is achieved via a novel DP K-means Nyström method, enabling efficient and accurate model training for general kernels while safeguarding privacy.

Debiasing synthetic data generated by deep generative models enhances statistical convergence rates, yielding reliable results for specific analyses.

New definitions of differential privacy enable machine learning algorithms to credit sources appropriately, balancing data utility and copyright compliance.

Continual counting with gradual privacy expiration: A new algorithm achieves optimal accuracy with exponentially decaying privacy!

This paper introduces a novel machine unlearning method using projected noisy stochastic gradient descent, providing the first approximate unlearning guarantee under convexity, significantly improving…

New efficient attack reveals GNN model training data properties.

This paper introduces BSR, a novel banded square root matrix factorization for differentially private model training. Unlike existing methods, BSR avoids computationally expensive optimization, enabli…

New metrics audit label privatization, revealing differentially private schemes often outperform heuristic methods in the privacy-utility tradeoff.

Boosting machine learning model accuracy in privacy-preserving applications, this research introduces novel noise calibration methods directly targeting desired attack risk levels, bypassing conventio…

Huber loss minimization ensures accurate and robust mean estimation under user-level differential privacy, especially for imbalanced datasets and heavy-tailed distributions.