differential-privacy

This paper proposes causal workloads—differentially private query sets based on orthogonal moments—to enable valid causal inference from synthetic data, introducing methods like Causal-AIM and noise-aware multiple imputation.

This paper introduces DP-NGD, a practical framework that integrates natural gradient descent with differential privacy by decoupling curvature estimation from private data and reconciling isotropic DP constraints with anisotropic second-order optimization, achieving state-of-the-art accuracy and up to 10x convergence speedup under the same privacy budget.

This paper formalizes behavioral privacy leakage in multi-round agentic negotiation and proposes an adaptive stochastic policy that provides differential privacy guarantees while maintaining high negotiation utility.

The Trump administration issued a directive banning modern privacy techniques like differential privacy from U.S. Census Bureau publications, threatening data accuracy and individual privacy. Cynthia Dwork and other researchers call on the scientific community to oppose the order.

This paper presents Privacy-Preserving Probabilistic Race/Ethnicity Estimation (PPRE), a method that combines privacy technologies including secure two-party computation, differential privacy, and additive homomorphic encryption to enable fairness measurements for U.S. LinkedIn members without exposing sensitive demographic data.

This paper introduces natural identifiers (NIDs) for post-hoc privacy auditing and dataset inference in large language models, eliminating the need for retraining or held-out datasets.

FedUP proposes a one-shot federated unlearning framework that uses lightweight, pluggable filters guided by differentially private class centroids to efficiently remove specific knowledge without multi-round communication, achieving low latency and inherent reversibility.

This research paper proposes a framework for fair token allocation and private data valuation in decentralized multi-modal agentic systems, using differentially private prototypes to balance privacy and utility while scheduling limited edge AI resources.

The U.S. Department of Commerce has ordered a ban on noise infusion in all statistical products from the Census Bureau and Bureau of Economic Analysis, which could undermine differential privacy protections and statistical accuracy.

DP-MacAdam combines adaptive clipping and adaptive momentum to improve differentially private SGD, achieving better model utility without manual tuning of the clipping threshold.

PE-means adapts the private evolution algorithm to differentially private k-means clustering, achieving a 20% average improvement in clustering loss over existing methods.

This paper introduces a new differential privacy sketching mechanism based on fast transforms that achieves state-of-the-art privacy guarantees and improved runtime, and applies it to DP linear regression to obtain the first fast method for DP ordinary least squares.

This paper solves a COLT open problem by providing an optimal gap-dependent regret algorithm for private stochastic decision-theoretic online learning, achieving the lower bound of order (log K)/Δ_min + (log K)/ε.

This paper proposes CE-FedGNN, a federated graph neural network framework that achieves communication efficiency and privacy preservation by infrequently exchanging aggregated node representations with metric differential privacy guarantees, and demonstrates strong performance on benchmarks.

This paper proves a finite-sample bound on the approximate max-information of DP-SGD that is at most linear in dataset size, yielding PAC-Bayes generalization bounds for models trained with differential privacy.

This paper introduces PACE-GGM, a differentially private method for covariance estimation that adaptively selects and measures the most informative entries of the empirical covariance matrix, using Gaussian graphical models for reconstruction. It shows improved estimation error over baselines on real-world data, especially in high-dimensional settings.

This note presents a research moment where Codex helped find a new rare-switching rule for private linear bandits, using the generalized Rayleigh quotient to overcome the failure of determinant-based monotonicity due to Gaussian noise.

This paper introduces a framework that connects randomized smoothing to differential privacy through privacy profiles, enabling tight provable robustness guarantees against backdoor attacks that jointly affect training and inference. The approach is instantiated for DP-SGD and Deep Partition Aggregation with experiments on MNIST and CIFAR-10.

This paper establishes the first population risk bounds for Kolmogorov-Arnold Networks trained with mini-batch SGD and DP-SGD using correlated noise, advancing theoretical understanding of KANs in privacy-sensitive domains.

This paper presents a systematic evaluation of how differential privacy impacts social bias in large language models, finding that while it reduces bias in sentence scoring, the effect does not generalize across all tasks.