The paper introduces the Generalized Thresholding Mechanism (GTM) to solve the generalized private testing problem in differential privacy, achieving near-optimal accuracy and sample complexity guaran…

The paper addresses secure distributed hypothesis testing, proving impossibility in the standard setting and achieving secure testing for simple and general classes by incorporating a shared secret ke…

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

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

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…

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 the PML envelope, a novel definition that provides a robust and operationally meaningful measure of information leakage about a secret, satisfying both post-processing robustness…

The paper characterizes the minimax optimal excess-risk rate for pure $\varepsilon$-DP stochastic convex optimization with heavy-tailed gradients, providing an algorithm that achieves this rate.

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 quantifies the cost of privacy in language identification and generation using differentially private (DP) methods, finding that the cost is surprisingly mild, particularly absent under appr…

The paper develops a general framework to exactly characterize the composition of mechanisms satisfying multiple differential privacy constraints, extending known results to arbitrary numbers of const…

The paper proposes a Quantitative Information Flow (QIF) framework to systematically and rigorously compare Local Differential Privacy (LDP) frequency estimation protocols, moving beyond simple $\vare…

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 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 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…

The paper proposes a robust causal decision framework to measure advertising incrementality despite multiple sources of privacy-induced signal degradation, providing certified decisions on the strengt…

The paper provides a tight, transparent, and closed-form analysis of the trade-off function for Differentially Private SGD using random shuffling, significantly improving upon previous methods and est…

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 analyzes differential privacy auditing as a bilevel game, showing that naive audit designs fail to detect true harm when developers strategically respond, and proposes an optimal, single-le…

The paper introduces PolyVeil, a protocol for private Boolean summation that uses permutation matrices in the Birkhoff polytope, achieving strong security guarantees while highlighting a fundamental t…