#differential-privacy

11 curated events

papersTODAY 04:00 UTC

Differential Privacy of Gaussian Process Posterior Sampling

This paper studies the privacy guarantees of releasing posterior sample paths from a Gaussian process when the entire training set, including covariates and responses, is considered private. Rather than relying on standard differential-privacy mechanisms that add external noise, the analysis focuses on the randomness inherent in posterior sampling itself. It provides a formal treatment of how much privacy such released sample paths preserve.

differential-privacy/gaussian-processes/privacy/research

papersTODAY 04:00 UTC

CANAL adds channel-aware noise allocation for private medical image segmentation

A new arXiv paper proposes CANAL, a method that lets hospitals train segmentation models together without sharing patient scans. It uses knowledge distillation to transfer learned feature representations, injecting differential-privacy noise in a channel-aware way so that the privacy budget is spent where it matters most. The approach targets medical image segmentation, where complementary data sits in separate institutions that cannot legally exchange it.

differential-privacy/medical-imaging/knowledge-distillation/federated-learning

papersTODAY 04:00 UTC

Federated Learning Framework Combines Differential Privacy and Homomorphic Encryption

A new arXiv paper presents a federated learning framework designed to make collaborative training across distributed data safer. It combines dynamic differential privacy, a lightweight homomorphic encryption scheme, and asynchronous aggregation to reduce privacy risks while limiting computational overhead. The work is posted as a preprint and has not yet been peer reviewed.

federated-learning/privacy/differential-privacy/encryption

papersTODAY 04:00 UTC

Compact Adaptation Method Aligns Personalized Federated Learning with Record-Level Privacy

A new arXiv paper examines how record-level differential privacy clashes with personalized federated learning, where each client's individual variation is low-dimensional but repeated training rounds broadcast high-dimensional model updates. The authors propose a compact adaptation approach paired with variable-length Gaussian communication to reduce that mismatch. The work targets more efficient and privacy-preserving personalization across federated clients.

federated-learning/differential-privacy/research/personalization

papersTODAY 04:00 UTC

SpliTEE combines trusted hardware with differentially private GPU offloading for LLM inference

A new paper proposes SpliTEE, a system that runs large language model inference partly on trusted hardware while outsourcing the rest to GPUs with differential privacy guarantees. The approach aims to keep user prompts confidential, addressing risks such as sensitive data being memorized during retraining by remote model providers. It targets a balance between privacy protection and inference performance.

privacy/llm-inference/differential-privacy/trusted-hardware

papersTODAY 04:00 UTC

Auditing User-Level Privacy in Private Evolution Synthetic Data

A new arXiv paper examines how to audit user-level privacy guarantees in Private Evolution, a method for generating synthetic data in federated settings. The approach collects clipped user votes over a shared candidate bank and turns them into a differentially private histogram with calibrated noise. The work focuses on verifying that individual users' raw data remains protected under this mechanism.

privacy/differential-privacy/synthetic-data/federated-learning

papersSEP 11 04:00 UTC

DP-Muon: Differentially Private Optimization with Matrix-Orthogonalized Momentum

A new arXiv paper introduces DP-Muon, an optimizer that combines matrix-orthogonalized momentum with differential privacy guarantees. The method relies on standard per-example gradient clipping and releases one Gaussian-noised gradient per step, treating the matrix and auxiliary updates as post-processing. The authors present a convergence analysis for this approach.

differential-privacy/optimization/research/training

papersSEP 11 04:00 UTC

PEARL Framework Evaluates Differentially Private Synthetic Educational Data

A new arXiv paper introduces PEARL, a task-aware framework for assessing differentially private synthetic data generated from learner records. The work targets personalized learning systems, where performance, behavioral, and demographic data are highly sensitive. PEARL aims to measure how well such synthetic data supports downstream educational tasks while preserving privacy.

differential-privacy/synthetic-data/education/evaluation

papersSEP 10 04:00 UTC

New Method Estimates Treatment Effects Under Differential Privacy

A researcher proposes a technique for estimating average treatment effects in observational studies while preserving differential privacy. The approach uses propensity score blocking to group similar subjects, limiting how much any individual's data influences the result. The preprint is posted on arXiv and is categorized under machine learning.

privacy/differential-privacy/causal-inference/research

papersSEP 10 04:00 UTC

Study Audits Subgroup Privacy Risks in Differentially Private Synthetic Text

A new paper introduces an auditing framework that runs membership inference attacks at the subgroup level against synthetic text produced under differential privacy. It explores whether formal worst-case privacy guarantees hold up in practice for smaller groups represented in the underlying data. The work offers data publishers a way to gauge real-world leakage before sharing synthetic text in place of sensitive datasets.

privacy/synthetic-data/differential-privacy/auditing

papersSEP 10 04:00 UTC

Paper Revisits Whether Neural Networks Can Match Statistical Models for DP Tabular Synthesis

A revised arXiv paper challenges the widely held view that statistical methods outperform neural networks when generating differentially private tabular data. The authors argue that this conclusion glosses over cases involving densely correlated data, where neural approaches may be more effective. The submission is an updated version of previously posted research.

differential-privacy/tabular-data/synthetic-data/research