The paper introduces a differentially private manifold denoising framework that allows noisy, non-private query points to be corrected using sensitive reference data while providing formal $(\varepsil…

The paper proposes a novel data transformation framework that creates semantically rich, privacy-preserving numeric views of EHR data, enabling large-scale research while provably breaking patient lin…

The paper proposes using geometric metrics, specifically eigenspace alignment, to monitor the structural integrity of large behavioral populations, demonstrating its effectiveness in detecting network…

RootGuard introduces a dependency-aware privacy mechanism that sanitizes private data roots once, ensuring consistent privacy guarantees across multiple multi-turn agent interactions, significantly ou…

This paper proposes a principled, theoretically derived rule for selecting the optimal grid size in differentially private non-interactive K-Means clustering, improving accuracy over existing empirica…

VeriX-Anon is a multi-layered framework that provides mathematically verifiable assurance that outsourced data anonymization (k-anonymization) was executed correctly, achieving high detection rates ag…

This case study systematically measures how placing anonymization at different points (dataset vs. generated answer) within the RAG pipeline affects the privacy-utility trade-off, demonstrating that p…

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 introduces Zero-Run privacy auditing, a post-hoc framework that allows for practical differential privacy evaluation of large, deployed models without requiring retraining or controlled data…

This paper introduces a novel privacy mechanism, the geometry-aware Mahalanobis norm planar Laplace (MNPL) mechanism, to provide formal location privacy guarantees for channel charting used in locatio…

The paper proposes FI-LDP-HGAT, a novel framework that combines a hierarchical graph attention network with feature-importance-aware anisotropic differential privacy to enable high-utility, privacy-pr…

The paper introduces PE-means, an improved differentially private $k$-means clustering method that uses the Private Evolution (PE) algorithm to achieve better clustering loss compared to existing stat…

The paper proposes GeoMark, a geometry-aware localized watermarking framework that robustly protects Embedding-as-a-Service (EaaS) against model stealing and copyright infringement while preserving ut…

The paper introduces Appraisal, a novel Screening-then-Linkage framework (PPRS) that significantly improves the scalability and efficiency of Privacy-Preserving Record Linkage by incorporating a light…

The paper proposes a Jacobian-guided anisotropic noise reshaping technique to selectively attenuate noise in task-relevant subspaces, significantly enhancing data utility while maintaining Local Diffe…

The paper proposes CAMP, a cross-turn privacy framework that mitigates Cumulative PII Exposure (CPE) in multi-turn LLM conversations by tracking and masking accumulated personal data across the entire…

The paper introduces 'contrastive privacy,' a formal, model-agnostic, and quantitative method for evaluating the semantic success of AI-based sanitization across multiple media modalities.

The paper proposes a Secure Parallel Determinant Computation (SPDC) framework that enables efficient, privacy-preserving, and scalable matrix determinant calculation across multiple untrusted edge ser…

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 introduces PAS, a structured privacy mechanism that encodes user location using relative anchors, enabling location privacy in spatial RAG systems while maintaining high retrieval performanc…