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

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 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.

This paper analyzes the bid-ask spread and welfare in the Glosten-Milgrom model when the market maker observes a noisy, privacy-protected trade direction signal, deriving a specific 'privacy subsidy'…

The paper proposes a novel exponential mechanism using quadratic approximations to fine-tune machine learning models on sensitive data while providing strong differential privacy guarantees.

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

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

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 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 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 derives the unique linear Kyle equilibrium and identifies a closed-form 'privacy subsidy'—the break-even fee—for cryptocurrency exchanges that use Gaussian noise to obscure order flow.

This paper demonstrates that the classical discrete Laplace mechanism can be post-processed to create versatile, unbiased estimators for various subexponential functions, making it a preferred choice…

The paper proposes DPSR-CG, a novel differentially private selective release mechanism that rigorously maintains strict privacy guarantees while significantly improving model utility compared to exist…

LAPRAS proposes a learning-augmented differentially private query answering framework that uses predictions of future queries to maximize utility while maintaining robustness against prediction errors…

The paper evaluates LLM-based simulators for generating differentially private synthetic data, finding that while they show promise for utility, they suffer from significant distribution drift due to…

The paper introduces 'mixture mechanisms,' a novel class of additive noise mechanisms that achieve approximate differential privacy by mixing multiple Gaussian distributions, resulting in lower noise…

The paper introduces 'mixture mechanisms,' a novel class of additive noise mechanisms that achieve differential privacy for real-valued queries, significantly reducing noise compared to the standard G…

This paper extends the privacy subsidy concept from the single-period Kyle model to continuous time, deriving a closed-form expression for the cumulative expected transfer (privacy subsidy) in a conti…

This paper develops a differential privacy framework to analyze and optimize privacy leakage from AI agent responses that utilize sensitive enterprise data, focusing on deriving optimal generation par…