Search: “privacy”

Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data

arXiv:2607.15606v2 Announce Type: replace Abstract: Synthetic sequential tabular data are increasingly used for privacy-preserving data sharing and data-driven research, but evaluating their fidelity

12 Aug 2026

Human

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Applications

Quantifying Membership Disclosure Risk for Tabular Synthetic Data Using Kernel Density Estimators

DGX agent

arXiv:2603.10937v2 Announce Type: replace Abstract: The use of synthetic data has become increasingly popular as a privacy-preserving alternative to sharing real datasets, especially in sensitive doma

applicationsarxiv-cs-lg

11 Aug 2026

FedTransKD-IDS: Robust Federated Transfer Learning with Knowledge Distillation for Intrusion Detection in IoT

arXiv:2608.06447v1 Announce Type: cross Abstract: In modern distributed network environments, particularly in Internet of Things infrastructures and 5G networks, stringent privacy preservation and sca

10 Aug 2026

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging

model-releasesarxiv-cs-cv

arXiv:2608.01094v1 Announce Type: new Abstract: Federated learning enables medical-imaging models to be trained across hospitals, and privacy law, most explicitly the GDPR ``right to be forgotten'', t

4 Aug 2026

Communication-Efficient Secure Aggregation in Decentralized Learning

arXiv:2405.07708v3 Announce Type: replace Abstract: Decentralized learning (DL) enables participants to collaboratively train models without a central server, yet it faces significant scalability chal

3 Aug 2026

Have I Seen You? Embedding Behavior Signals Synthetic Face Dataset Membership

arXiv:2607.29144v1 Announce Type: cross Abstract: Synthetic face datasets are increasingly used to reduce privacy exposure and data access constraints in biometric recognition. Yet the generators that

researcharxiv-cs-ai

3 Aug 2026

Now You Have My Healthy Attention: A U-DiT for Brain-MRI Inpainting

arXiv:2607.27974v1 Announce Type: new Abstract: The ASNR-MICCAI BraTS Local Synthesis (Inpainting) task asks for the anatomically plausible completion of healthy brain tissue within a masked region of

local-aiarxiv-cs-cv

31 Jul 2026

A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data

arXiv:2509.10517v3 Announce Type: replace Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use. Federated Le

27 Jul 2026

A Drift Stable Quantum Federated Learning for Intelligent Services

arXiv:2607.21647v1 Announce Type: new Abstract: Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-awar

local-aiarxiv-cs-lg

27 Jul 2026

Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification

arXiv:2607.21839v1 Announce Type: cross Abstract: Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing

27 Jul 2026

PersGuard: Preventing Malicious Personalization in Text-to-Image Diffusion Models via Model Backdoors

arXiv:2502.16167v2 Announce Type: replace-cross Abstract: Diffusion models (DMs) have advanced text-to-image (T2I) synthesis, yet their personalization capabilities raise serious privacy and copyright

researcharxiv-cs-ai

16 Jul 2026

Benchmark Evaluation of Feredated Learning on Multi-organ Images

model-releasesarxiv-cs-cv

arXiv:2607.08219v1 Announce Type: new Abstract: The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI. F

10 Jul 2026

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

arXiv:2607.08122v1 Announce Type: new Abstract: Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic

10 Jul 2026

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

arXiv:2607.06612v1 Announce Type: cross Abstract: Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing use

local-aiarxiv-cs-ai

9 Jul 2026

Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering

arXiv:2306.12859v3 Announce Type: replace Abstract: Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computi

9 Jul 2026

Prima.cpp: Fast 30-70B LLM Inference on Heterogeneous and Low-Resource Home Clusters

arXiv:2504.08791v3 Announce Type: replace-cross Abstract: On-device inference offers privacy, offline use, and instant response, but consumer hardware restricts large language models (LLMs) to low thr

7 Jul 2026

Assessing VLM Reliability for Medical Image Quality Evaluation Under Corruption and Bias

arXiv:2607.01973v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly applied in medical tasks such as pathology description, report generation, and visual question answerin

3 Jul 2026

Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification

arXiv:2607.01272v1 Announce Type: cross Abstract: Deploying 3D point cloud analysis in privacy-sensitive, resource-constrained settings faces two barriers: data cannot be centralized, and models must

3 Jul 2026

A Filtered Mixture-of-Generators for Fully Synthetic Survival Training

arXiv:2607.00127v1 Announce Type: new Abstract: Survival analysis models time-to-event data, but in clinical settings training data are costly and scarce: events accrue over years of follow-up, cohort

2 Jul 2026

Hardware

Hardware-Rooted AI Security That Won’t Slow You Down

NVIDIA Confidential Computing addresses data privacy and security concerns for AI workloads by protecting data during inference and engagement with models, offering high-performance protection in loca

hardwarenvidia-developer

2 Jul 2026

Tutorials

Phantom: A Unified Face-Swap Deepfake Protection Framework with Latent and Spatial Constraints

arXiv:2606.31703v1 Announce Type: new Abstract: Face-swapping deepfakes pose an escalating threat to personal privacy by enabling unauthorized identity manipulation. While adversarial approaches have

tutorialsarxiv-cs-cv

1 Jul 2026

TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling

arXiv:2606.31268v1 Announce Type: cross Abstract: The growing demand for privacy-preserving data sharing has positioned synthetic data generation as a critical component of responsible AI workflows. D

safetyarxiv-cs-ai

1 Jul 2026

Industry

Google warns EU's plans to weaken its monopoly could expose user data

Google argued that the European Commission's proposal to require sharing search data with third parties would 'force us to hand this data over to third parties, with dangerously ineffective privacy pr

industryars-technica

29 Jun 2026

Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning

arXiv:2511.20196v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can inadvertently memorize privacy-sensitive information during training. While existing unlearning methods

29 Jun 2026

Applications

Federated Hash Projected Latent Factor Learning

arXiv:2606.26192v1 Announce Type: new Abstract: Hash Learning (HL) is an efficient representation learning approach that maps real-valued data into compact binary representations. Traditional HL metho

applicationsarxiv-cs-lg

26 Jun 2026

Quantization in Federated Learning: Methods, Challenges and Future Directions

arXiv:2606.26822v1 Announce Type: new Abstract: Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally co

local-aiarxiv-cs-lg

26 Jun 2026

Tutorials

A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input

arXiv:2505.14251v2 Announce Type: replace Abstract: We study the problem of differentially private second moment estimation and present a new algorithm that achieve strong privacy-utility trade-offs e

tutorialsarxiv-cs-lg

24 Jun 2026

Applications

Detecting and Understanding Vulnerabilities in Fully Homomorphic Encryption Frameworks

arXiv:2606.22519v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) allows computations to be performed directly on encrypted data without decryption, offering strong privacy guarante

applicationsarxiv-cs-lg

23 Jun 2026

MedFedPure: A Medical Federated Framework with MAE-based Detection and Diffusion Purification for Inference-Time Attacks

arXiv:2511.11625v2 Announce Type: replace Abstract: Artificial intelligence (AI) has shown great potential in medical imaging, particularly for brain tumor detection using Magnetic Resonance Imaging (

local-aiarxiv-cs-lg

23 Jun 2026

Robust and Differentially Private Principal Component Analysis

arXiv:2507.15232v2 Announce Type: replace-cross Abstract: Recent advances have sparked significant interest in the development of privacy-preserving Principal Component Analysis (PCA). However, many e

23 Jun 2026

A Comparative Study of Student Perspectives on Technical Writing Feedback Quality: Evaluating LLMs, SLMs, and Humans in Computer Science Topics

arXiv:2601.11541v2 Announce Type: replace-cross Abstract: To address the scalability of feedback in computer science while mitigating the privacy and cost limitations of commercial Large Language Mode

9 Jun 2026

Domain-Adapted Small Language Models with Hybrid Post-Processing: Achieving Cost-Efficient, Low-Latency Multi-Label Structured Prediction via LoRA Fine-Tuning on Scarce Data

arXiv:2606.05781v2 Announce Type: replace Abstract: Deploying frontier large language models (LLMs) for domain-specific structured evaluation tasks incurs prohibitive latency, cost, and data-privacy o

9 Jun 2026

SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)

arXiv:2606.08372v1 Announce Type: cross Abstract: Synthetic data is increasingly promoted as a privacy-preserving substitute for releasing sensitive tabular records, yet its central adversarial threat

9 Jun 2026

Accelerating Reproducible Research in Synthetic EHR Generation

arXiv:2606.06990v1 Announce Type: new Abstract: The generation of high-fidelity synthetic Electronic Health Records (EHR) is crucial for advancing medical research while preserving patient privacy. Ho

8 Jun 2026

Differentially Private Joint Independence Test

arXiv:2503.18721v3 Announce Type: replace-cross Abstract: Identification of joint dependence among several random vectors plays an important role in many statistical applications, where the data may c

4 Jun 2026

LifeSide: Benchmarking Agents as Lifelong Digital Companions

model-releasesarxiv-cs-cl

arXiv:2606.04660v1 Announce Type: new Abstract: Lifelong digital companions must integrate cross-session cues, continually update their understanding of users, and adapt to shifting privacy boundaries

4 Jun 2026

Impact of Graph Structure on Membership-Inference Risk for Graph Neural Networks

arXiv:2601.17130v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are widely used for tasks such as node classification and link prediction, but their use in sensitive settings raises c

3 Jun 2026

The Impact of Temporal Granularity on Socio-Demographic Inference from Household Load Profiles

arXiv:2606.03358v1 Announce Type: new Abstract: Smart meter data can reveal sensitive socio-demographic characteristics of households, raising privacy concerns. While this risk has been demonstrated a

3 Jun 2026

Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation

arXiv:2512.16310v3 Announce Type: replace-cross Abstract: LLM-based agents increasingly use multiple external tools to complete complex tasks. We study Tools Orchestration Privacy Risk (TOP-R): an age

2 Jun 2026

Near-Optimal Private Tests for Simple and MLR Hypotheses

arXiv:2601.21959v2 Announce Type: replace-cross Abstract: We develop a near-optimal testing procedure under the framework of Gaussian differential privacy for simple as well as one- and two-sided test

2 Jun 2026

Silent Failures in Federated Personalization of Foundation Models

arXiv:2606.00947v1 Announce Type: cross Abstract: Foundation models are increasingly personalized on decentralized private data through federated learning and are now deployed at scale under growing r

safetyarxiv-cs-ai

2 Jun 2026

Differentially Private Preference Data Synthesis for Large Language Model Alignment

arXiv:2605.30808v1 Announce Type: cross Abstract: Preference alignment is a crucial post-training step for large language models (LLMs) to ensure their outputs align with human values. However, post-t

safetyarxiv-cs-ai

Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences

arXiv:2605.30873v1 Announce Type: cross Abstract: Federated Learning (FL) offers a privacy-preserving pathway for aligning Large Language Models (LLMs); however, existing frameworks typically enforce

local-aiarxiv-cs-ai

From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents

arXiv:2603.18382v2 Announce Type: replace Abstract: Anonymization is often assumed to protect privacy once explicit identifiers are removed, because re-identification has historically required special

Sequential Subspace Noise Injection Prevents Accuracy Collapse in Certified Unlearning

arXiv:2601.05134v2 Announce Type: replace Abstract: Certified unlearning based on differential privacy offers strong guarantees but remains largely impractical: the noisy fine-tuning approaches propos

Computationally Efficient Replicable Learning of Parities and Applications

arXiv:2602.09499v2 Announce Type: replace Abstract: We study the computational relationship between replicability (Impagliazzo et al. [STOC `22], Ghazi et al. [NeurIPS `21]) and other stability notion

29 May 2026

Applications

From GPS Points to Travel Patterns: Flexible and Semantic Trajectory Generation with LLMs

arXiv:2605.30014v1 Announce Type: new Abstract: Urban trajectories play a crucial role in modeling urban dynamics and supporting various smart city applications. However, privacy concerns restrict acc

applicationsarxiv-cs-ai

29 May 2026

FedEHR-Gen: Federated Synthetic Time-Series EHR Generation via Latent Space Alignment and Distribution-Aware Aggregation

arXiv:2605.27892v1 Announce Type: new Abstract: Synthetic Electronic Health Record (EHR) generation provides a promising avenue for data augmentation and cross-hospital modeling in privacy-constrained