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 a differentially private manifold denoising framework that allows noisy, non-private query points to be corrected using sensitive reference data while providing formal $(\varepsil…

The paper proposes PINA, a two-stage differentially private clustered federated learning framework that improves convergence and robustness by using low-rank adaptation and a normality-driven aggregat…

The paper proposes a novel two-stage framework to differentially privatize tables of counts by focusing on preserving the accuracy of the underlying count distribution, introducing the specialized cyc…

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

This paper introduces a new algorithm for estimating $k$-clique counts with differential privacy, which is scalable and significantly faster than previous methods.

This paper lays the theoretical foundation for incorporating public data into differential privacy mechanisms for improved synthetic data generation.

This paper introduces a novel framework for differentially private sampling by using the Wasserstein distance as the utility measure, proposing the Wasserstein Projection Mechanism (WPM) to address li…

This paper introduces private and efficient algorithms for exact community detection in the stochastic block model under privacy constraints, achieving near-linear time and space complexity.

This paper provides a comprehensive, practitioner-oriented framework and survey to guide the selection and evaluation of differentially private methods for releasing sensitive graph data.

This paper addresses the challenging problem of multi-objective submodular maximization under a cardinality constraint while ensuring differential privacy, proposing novel algorithms with approximatio…

This paper introduces DP-DiPP, a compression pipeline for differentially private image data using stochastic codes and diffusion models, achieving significant compression rates while retaining compara…

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…

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…

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.

The paper proposes eSpat-B and eSpat+ systems to enable efficient and privacy-preserving distribution statistics analysis on massive, dynamic mobile spatial data.

This paper proposes a high entropy differential privacy implementation that maintains aggregation invariants with probability one or exponentially close to one.

The paper demonstrates that for mean estimation under differential privacy, the benefits of fully personalized privacy budgets are often limited, suggesting that choosing the correct effective budget…

The paper proposes PAC-DP, a personalized adaptive clipping framework that dynamically adjusts gradient clipping thresholds based on the desired privacy budget, significantly improving the privacy-uti…