differential-privacy

This paper demonstrates that causal fairness mechanisms, specifically edge cuts on causal graphs, are portable across various synthetic data generator families including GANs and diffusion models, with minimal impact on data fidelity and utility.

The paper reveals a privacy-hallucination tradeoff in differentially private language models, where stricter privacy budgets increase hallucination risks and explores mitigation strategies.

The paper proposes a lightweight defense framework to enhance the empirical privacy of DP-SGD without theoretical privacy cost, validated through extensive audits across models, datasets, and threat models.

The paper investigates the geometric challenges in merging differentially private task models and introduces DP-Merging, a framework to enhance mergeability while maintaining privacy guarantees.

This paper introduces Private Best-of-N (PrivBoN) and Private Inference-Time Pessimism (PrivITP) methods that add calibrated noise to reward scores in inference-time alignment to achieve differential privacy and mitigate reward hashing, with minimal additional alignment cost.

The US Commerce Department is circulating a proposal to stop collecting census data on race and sexual orientation, and to exclude undocumented immigrants from the count for the 2030 census, which could reshape federal funding and political representation.

This paper introduces StraightDP, a geometry-aware differential privacy framework for text-conditioned rectified-flow transformers. It partitions the privacy budget to release class-conditional moments and use DP-SGD, improving accuracy and FID over uniform DP training at strong privacy levels.

Noisegate is an open-source differential-privacy gateway that allows untrusted AI agents to query sensitive datasets with mathematical guarantees against record leakage. It features built-in attack testing, validated noise mechanisms, and acts as an MCP server for Claude Desktop.

DP-FedSOFIM moves curvature estimation to the server in differentially private federated learning, achieving the same privacy guarantee as DP-FedGD with O(d) client memory and significant early-round accuracy gains.

This paper introduces a mean-field privacy game framework for federated learning, enabling tractable Nash equilibrium analysis for arbitrarily many clients with heterogeneous privacy preferences and yielding a personalized privacy guarantee.

Proposes a unified post-hoc detection framework for copyright infringement in AI models, using conditional sensitivity and differential privacy to measure memorization across modalities.

This paper proposes causal workloads—differentially private query sets based on orthogonal moments—to enable valid causal inference from synthetic data, introducing methods like Causal-AIM and noise-aware multiple imputation.

This paper introduces DP-NGD, a practical framework that integrates natural gradient descent with differential privacy by decoupling curvature estimation from private data and reconciling isotropic DP constraints with anisotropic second-order optimization, achieving state-of-the-art accuracy and up to 10x convergence speedup under the same privacy budget.

This paper formalizes behavioral privacy leakage in multi-round agentic negotiation and proposes an adaptive stochastic policy that provides differential privacy guarantees while maintaining high negotiation utility.

The Trump administration issued a directive banning modern privacy techniques like differential privacy from U.S. Census Bureau publications, threatening data accuracy and individual privacy. Cynthia Dwork and other researchers call on the scientific community to oppose the order.

This paper presents Privacy-Preserving Probabilistic Race/Ethnicity Estimation (PPRE), a method that combines privacy technologies including secure two-party computation, differential privacy, and additive homomorphic encryption to enable fairness measurements for U.S. LinkedIn members without exposing sensitive demographic data.

This paper introduces natural identifiers (NIDs) for post-hoc privacy auditing and dataset inference in large language models, eliminating the need for retraining or held-out datasets.

FedUP proposes a one-shot federated unlearning framework that uses lightweight, pluggable filters guided by differentially private class centroids to efficiently remove specific knowledge without multi-round communication, achieving low latency and inherent reversibility.

This research paper proposes a framework for fair token allocation and private data valuation in decentralized multi-modal agentic systems, using differentially private prototypes to balance privacy and utility while scheduling limited edge AI resources.

The U.S. Department of Commerce has ordered a ban on noise infusion in all statistical products from the Census Bureau and Bureau of Economic Analysis, which could undermine differential privacy protections and statistical accuracy.