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.

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 analyzes information-sharing mechanisms in oligopolies, finding that privacy protection alone is insufficient to incentivize suppliers to share data; successful sharing requires combining pr…

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

The paper demonstrates that current transfer-based AML systems fail in complex DeFi environments because economic value migration can be structurally decoupled from explicit token transfers.

The paper analyzes the potential market impact of a large, unknown Bitcoin holder (the Satoshi overhang) and concludes that the mechanical downside risk is bounded, suggesting the terminal states are…

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 develops improved Gaussian mechanisms for Rényi Pufferfish Privacy (RPP) by incorporating Gaussian and Gaussian-mixture priors, significantly reducing the required noise and improving the p…

The paper designs an optimal mechanism for soliciting expensive computational tasks in adversarial blockchain environments, showing that the loss of optimality scales logarithmically with the cost of…

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

The paper proves that for resources with structural parallelizability (like divisibility and transferability), it is impossible to enforce a linear cost for concentrating influence, demonstrating that…

The paper introduces KTD-Fin, a novel benchmark that evaluates LLM trading agents by masking historical market data and decomposing returns, finding that LLM agents' profits are largely due to passive…