Search: “privacy”

RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments

arXiv:2609.05403v1 Announce Type: cross Abstract: Retail search systems serve diverse geographic regions with distinct query patterns, vocabularies, and product preferences, creating significant data

7 Sept 2026

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Applications

From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning

DGX agent

arXiv:2609.02984v1 Announce Type: new Abstract: The conventional approach to machine learning, that is, collecting data, training models, and performing inference in a single location, faces fundament

applicationsarxiv-cs-lg

4 Sept 2026

OSR: Output Space Redistribution for Adaptive Label Removal in Classification Models

arXiv:2609.03972v1 Announce Type: new Abstract: Label removal occurs frequently in classification systems with evolving taxonomies, where categories must be dynamically updated or eliminated. To accom

4 Sept 2026

Membership Inference in Fine-tuned Diffusion Language Models via Token-level Memorization Asymmetry

arXiv:2609.00873v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as an alternative modeling paradigm to autoregressive LMs, offering advantages such as parallel g

researcharxiv-cs-cl

2 Sept 2026

AIM: Anchor Identity Features, Then Match for Multimodal Large Language Model Unlearning

arXiv:2608.28312v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) can memorize identity-specific facts about people in their fine-tuning data, creating privacy risks when a pe

researcharxiv-cs-cl

31 Aug 2026

Graph-Guided Selective Unlearning for Language Models: Controlling Support Routes Beyond Forget Seeds

arXiv:2608.26743v1 Announce Type: new Abstract: Enterprises fine-tune language models on proprietary data that may later require removal due to privacy, contractual, or compliance obligations. Selecti

28 Aug 2026

Private and interpretable clinical prediction with quantum-inspired tensor train models

arXiv:2602.06110v2 Announce Type: replace Abstract: Publicly available clinical machine learning models pose an underappreciated privacy risk: their parameters or outputs can be exploited to recover i

28 Aug 2026

Your Voice Cloning System is Secretly a Voice Anonymizer

arXiv:2608.27360v1 Announce Type: new Abstract: Speaker anonymization suppresses speaker-identifying attributes from speech while preserving linguistic content and quality. We propose repurposing XTTS

researcharxiv-cs-cl

28 Aug 2026

Coordination on a Budget: Federated Active Learning with Few Labels

arXiv:2608.18634v1 Announce Type: new Abstract: Federated Active Learning (FAL) addresses the dual challenges of data privacy and label scarcity, where the absence of a global data view introduces add

20 Aug 2026

Community Concealment from Graph Neural Networks

arXiv:2602.12250v2 Announce Type: replace Abstract: Graph neural networks (GNNs) enable powerful unsupervised learning of communities. However, such inference may inadvertently expose sensitive group

19 Aug 2026

Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols

arXiv:2507.19894v2 Announce Type: replace Abstract: With the rapid advancement of generative models, privacy, copyright, safety, and reliability risks have attracted growing attention. To mitigate the

safetyarxiv-cs-lg

18 Aug 2026

Model Inversion Attacks: A Survey of Approaches and Countermeasures

arXiv:2411.10023v3 Announce Type: replace Abstract: Deep neural networks have enabled numerous studies and applications on both Euclidean data, such as images and text, and non-Euclidean data, such as

18 Aug 2026

Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directions

arXiv:2608.13844v1 Announce Type: cross Abstract: Large language models (LLMs) have become core components of cloud-based intelligent services in academia and industry, yet their training and deployme

researcharxiv-cs-ai

17 Aug 2026

Noise as a Probe: Membership Inference Attacks on Diffusion Models Leveraging Initial Noise

arXiv:2601.21628v2 Announce Type: replace-cross Abstract: Diffusion models have achieved remarkable progress in image generation, but their increasing deployment raises serious concerns about privacy

14 Aug 2026

Confidence Calibration of Deep Learning Systems

arXiv:2608.12100v1 Announce Type: cross Abstract: In high-stakes applications, reliable confidence estimates are as important as the predictions themselves. Confidence calibration ensures that predict

safetyarxiv-cs-ai

13 Aug 2026

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

Applications

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

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

safetyarxiv-cs-lg

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

safetyarxiv-cs-lg

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