The paper proposes a Jacobian-guided anisotropic noise reshaping technique to selectively attenuate noise in task-relevant subspaces, significantly enhancing data utility while maintaining Local Diffe…

The paper introduces SMA-DP-SGD, a Spectral Memory-Aware Differential Privacy method that enhances standard DP-SGD by incorporating a memory branch derived from past noisy updates, improving model uti…

The paper demonstrates that by introducing carefully designed correlations among locally added noise variables, local differential privacy mechanisms can achieve an estimation cost matching the optima…

The paper introduces Fractional-Order Differentially Private Stochastic Gradient Descent (FO-DP-SGD), a mechanism that incorporates fractional memory into the gradient release process to improve priva…

This paper introduces an attack, PRIVX, demonstrating that even differentially private (DP) Graph Neural Network (GNN) explanations leak enough structural information to allow an adversary to accurate…

The paper introduces a geometry-aware framework for quantum differential privacy by aligning noise to the Quantum Fisher Information (QFI) eigenstructure, achieving significantly tighter privacy-utili…

The paper introduces novel, efficient differentially private algorithms for estimating monotone statistics, significantly improving sample complexity compared to existing methods.

The paper proposes FI-LDP-HGAT, a novel framework that combines a hierarchical graph attention network with feature-importance-aware anisotropic differential privacy to enable high-utility, privacy-pr…

The paper proposes ICSA, a robust anonymization technique that replaces PCA with invariant coordinate selection to improve data privacy protection, especially when the dataset contains outliers, outpe…

TADP-RME introduces a trust-adaptive differential privacy framework that enhances data system reliability by dynamically adjusting the privacy budget based on user trust and disrupting geometric struc…

The paper introduces a novel realization-level privacy filtering approach that improves utility in differentially private data release by accounting for actual leakage rather than worst-case per-round…

This paper corrects the theoretical analysis of DP-SGD by identifying that common implementations, which use batch averaging, result in weaker privacy guarantees than previously reported.

This paper proposes a principled, theoretically derived rule for selecting the optimal grid size in differentially private non-interactive K-Means clustering, improving accuracy over existing empirica…

The paper introduces an optimal black-box auditing framework using Donsker-Varadhan estimators to estimate Rényi differential privacy (RDP) guarantees for machine learning algorithms.

This paper develops and analyzes two differentially private methods for answering counting queries on quantum-encoded datasets, demonstrating improved privacy guarantees and a quantum-safe approach fo…

The paper introduces Balanced Iteration Subsampling (BIS), a structured sampling scheme that is proven to achieve stronger privacy amplification than the standard Poisson subsampling used in DP-SGD by…

The paper introduces PE-means, an improved differentially private $k$-means clustering method that uses the Private Evolution (PE) algorithm to achieve better clustering loss compared to existing stat…

RootGuard introduces a dependency-aware privacy mechanism that sanitizes private data roots once, ensuring consistent privacy guarantees across multiple multi-turn agent interactions, significantly ou…

The paper develops a unified theoretical framework to systematically characterize the optimal privacy-utility trade-off (PUT) and optimal Local Differential Privacy (LDP) channels for general statisti…

This paper proposes two post-processing techniques, random selection and linear combination, to construct a model that satisfies any desired differential privacy level without retraining, given a set…