differential-privacy

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.

This paper evaluates LLM-based simulators as generators of differentially private synthetic data, using PersonaLedger to assess whether LLMs can faithfully reproduce statistical distributions from DP-protected personas. While achieving promising fraud detection utility (AUC 0.70 at ε=1), the study identifies significant distribution drift caused by systematic LLM biases that override input statistics.

Google and DeepMind introduce VaultGemma, a 1B-parameter open-source language model trained with differential privacy, accompanied by new scaling laws research that characterizes the compute-privacy-utility trade-offs in differentially private LLM training.

OpenAI presents PATE (Private Aggregation of Teacher Ensembles), a privacy-preserving approach that trains a student model on noisy outputs from multiple teacher models trained on disjoint datasets, providing strong differential privacy guarantees without exposing sensitive training data.