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'…

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…

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.

The paper simulates bargaining scenarios using LLM agents to analyze how optimizing agents for financial profit affects their honesty and trust, finding that while fine-tuning improves deal-making, it…

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…

This paper analyzes the trade-offs between privacy, efficiency, and targeting precision in aid allocation systems by studying private variants of both individual and unit-level allocation strategies.

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 proposes Information Sufficiency (IS) as a comprehensive framework for privacy-preserving LLM communication, demonstrating that free-text pseudonymization outperforms existing suppression an…

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 a mutagenic incentive intervention approach that mitigates collusion in embodied multi-agent systems by reshaping agents' payoff structures, effectively inducing defection and maint…

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 '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…

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 using Differentially Private (DP) synthetic data, specifically through tabular synthesis and DP-Seeded Agent-Based Modeling (ABM), to resolve the conflict between data utility and p…

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…

X-NegoBox introduces an explainable negotiation framework that adaptively manages privacy budgets for secure peer-to-peer energy data exchange, improving trust and reducing leakage.

The paper empirically investigates the lead marketing ecosystem, revealing a highly non-compliant system that aggressively collects, shares, and monetizes sensitive personal data through deceptive bro…

The paper addresses the over-reliance on GDPR in digital privacy research by systematically normalizing heterogeneous global data protection laws into a unified, data-lifecycle-aligned abstraction.

This study proposes a negotiation framework, using composite indices (RBTI and CATI), to explain how youth navigate competing privacy pressures when using smart voice assistants, finding that high usa…