Search: “privacy”

SkinAgent AI: A Safety-Grounded Multimodal Agentic Framework for Non-Diagnostic Skincare Support

arXiv:2609.29341v1 Announce Type: new Abstract: Consumer-facing skincare AI must coordinate visual evidence, product information, tool use, and user-facing actions within explicit evidence and safety

25 Sept 2026

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Applications

Structured Pose-Conditioned Flow Matching for Generative 5G CSI Augmentation

DGX agent

arXiv:2609.29912v1 Announce Type: cross Abstract: With the growing demand for privacy-preserving and occlusion-resilient human pose recognition (HPR), 5G channel state information (CSI) offers a promi

applicationsarxiv-cs-ai

25 Sept 2026

Tutorials

Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities

arXiv:2609.28722v1 Announce Type: new Abstract: While Federated Learning (FL) has been widely adopted for protecting user privacy in machine learning, it remains vulnerable to various robustness chall

tutorialsarxiv-cs-lg

25 Sept 2026

How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?

arXiv:2412.07813v4 Announce Type: replace-cross Abstract: To alleviate the training burden in federated learning while enhancing convergence speed, Split Federated Learning (SFL) has emerged as a prom

22 Sept 2026

HE-Guardrail: A Homomorphic Guardrail Against Jailbreak Attacks for Encrypted Large Language Model Inference

arXiv:2609.21484v1 Announce Type: cross Abstract: Homomorphic encryption (HE) has emerged as a promising approach to privacy-preserving machine learning (PPML), enabling computation directly over encr

21 Sept 2026

FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction

arXiv:2609.20026v1 Announce Type: new Abstract: Urban traffic forecasting often relies on information distributed across stakeholders who may be unable to share raw data due to privacy or commercial c

18 Sept 2026

Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems

arXiv:2609.19695v1 Announce Type: new Abstract: Federated learning (FL) enables privacy-preserving, on-device training across heterogeneous Internet-of-Things (IoT) deployments such as smart-city wate

18 Sept 2026

FedASAP: Activation Statistics-driven Structured Adaptive Pruning for Efficient Personalized Federated Learning for Lesion Segmentation on brain MRI

arXiv:2609.19042v1 Announce Type: cross Abstract: In medical imaging, developing robust deep learning models requires data from various domains. However, regulatory policies protecting patient privacy

17 Sept 2026

FedPGT: Progressive Gradient Transmission for Vehicular Federated Learning over Time-Varying Channels

arXiv:2609.18089v1 Announce Type: new Abstract: Vehicular federated learning (VFL) enables privacy-preserving collaborative model training for intelligent transportation systems, where communication r

17 Sept 2026

Tutorials

Coverage-Aware Virtual IMU Augmentation for Low-Resource Human Activity Recognition

arXiv:2609.16768v1 Announce Type: new Abstract: IMU-based human activity recognition (HAR) enables continuous, privacy-friendly monitoring of daily activities using wearable sensors. However, building

tutorialsarxiv-cs-ai

16 Sept 2026

Tutorials

EI-DDLGN: Efficient Encrypted Inference with Deep Differentiable Logic Gate Networks under TFHE

arXiv:2609.13636v1 Announce Type: cross Abstract: Privacy-preserving inference via Torus Fully Homomorphic Encryption (TFHE) provides strong protection for sensitive data in outsourced deep learning a

tutorialsarxiv-cs-lg

15 Sept 2026

Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Design

arXiv:2609.14591v1 Announce Type: new Abstract: Participation in federated learning (FL) comes at a cost. Clients trade off privacy, communication, and compute costs for potentially greater gains in m

15 Sept 2026

TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps

arXiv:2609.14762v1 Announce Type: cross Abstract: Cloud-hosted large language models (LLMs) are increasingly used for root cause analysis (RCA) in AIOps pipelines, but they introduce data privacy risk

15 Sept 2026

Almost Sure Convergence Analysis of Stochastic Gradient Methods with Clipping and Additive Noise

arXiv:2609.12119v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) with gradient clipping and additive noise has become a standard technique for training machine learning models, partic

safetyarxiv-cs-lg

14 Sept 2026

Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks

arXiv:2509.12386v3 Announce Type: replace-cross Abstract: Machine learning (ML) models are susceptible to various risks to security, privacy, and fairness. Most defenses are designed to protect agains

safetyarxiv-cs-ai

12 Sept 2026

CARTS: Contextual Autoregressive Rank Transcoding Steganography for Full-Capacity Keyed Text Encoding

arXiv:2609.10744v1 Announce Type: cross Abstract: Autoregressive language models can be used to transform a payload text into a stegotext of identical token length by preserving per-position rank info

Hardware

mmFHE: mmWave Sensing with End-to-End Fully Homomorphic Encryption

arXiv:2603.22437v2 Announce Type: replace-cross Abstract: We present mmFHE, the first system that executes the entire cloud-side mmWave sensing pipeline including the DSP and ML inference under fully

hardwarearxiv-cs-lg

On the Impact of Anonymization on the Performance of Large Language Models

arXiv:2609.11335v1 Announce Type: new Abstract: As large language models are increasingly deployed in sensitive domains, anonymizing input data to protect personally identifiable information has becom

researcharxiv-cs-cl

Subgroup Membership Inference Audits of Differentially Private Synthetic Text

arXiv:2609.09848v1 Announce Type: cross Abstract: Synthetic data releases are increasingly proposed in the literature as a means of sharing realistic data replicas in lieu of sensitive private dataset

When More Is Not Better: Component Anti-Synergy in a P300 Speller

arXiv:2609.10961v1 Announce Type: new Abstract: P300 brain-computer interface (BCI) spellers can provide hands-free communication for people with severe motor impairments. Modern pipelines combine mul

safetyarxiv-cs-lg

Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization

arXiv:2602.20773v2 Announce Type: replace Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and diff

Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs

arXiv:2609.10439v1 Announce Type: new Abstract: Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and

Kernel-Complexity Edge Sanitization for Training-Free Defense against Structural Graph Attacks

arXiv:2609.09698v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have achieved remarkable success across diverse applications, yet they remain highly vulnerable to adversarial attacks that

Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation

arXiv:2609.10264v1 Announce Type: cross Abstract: Open-source large language models (LLMs) are increasingly competitive with closed-source models while offering transparency and the ability to run inf

PELM: Power Efficient On-Device LLM Inference with Speculative Decoding and Dynamic Voltage Frequency Scaling

arXiv:2609.09662v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) directly on mobile platforms at the edge is gaining traction due to a myriad of benefits, such as increased pri

local-aiarxiv-cs-cl

BoundarySeg:Boundary-Aware Multi-Task Learning for Semi-Supervised 3D Medical Image Segmentation with Limited Unlabeled Data

arXiv:2505.09829v2 Announce Type: replace Abstract: Data scarcity is a primary bottleneck in 3D medical image segmentation: expert voxel-level annotations are expensive, and privacy regulations often

FANS: Federated Adaptive Network Search Learning for Heterogeneous Devices

arXiv:2609.06106v1 Announce Type: new Abstract: Heterogeneous Federated Learning (HFL) aims to train models across devices with diverse resource budgets while preserving data privacy. Existing HFL met

Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification

arXiv:2609.07403v1 Announce Type: new Abstract: Privacy-sensitive surveillance systems could benefit from large vision-language models (VLMs), but such models typically require centralized access to r

Fine-grained Distributed Backdoor Attacks in Federated Learning

arXiv:2609.07147v1 Announce Type: new Abstract: Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to centrali

TASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning

arXiv:2609.07444v1 Announce Type: new Abstract: The rise of privacy-preserving artificial intelligence (AI) has shifted the focus of model adaptation and personalization towards on-device learning, wh

LeanStream: A Speculate-and-Refine Streaming Framework for Efficient on-Device LLM Inference

arXiv:2609.03079v1 Announce Type: new Abstract: On-device LLM inference is attractive for privacy and responsiveness, but remains challenging on mobile and embedded devices because model weights far e

4 Sept 2026

Mind the Gap: Robustness Risks in PII Detection Systems

arXiv:2609.03464v1 Announce Type: new Abstract: Personally Identifiable Information (PII) detection is a foundational component of data protection infrastructure where missed entities constitute direc

4 Sept 2026

PragAlign: Feedback-Guided Pragmatic Alignment for Controlled Synthetic Dialogue Generation

arXiv:2609.02480v1 Announce Type: new Abstract: Synthetic dialogue generation can support research in privacy-restricted service settings, but generated conversations must preserve communicative inten

safetyarxiv-cs-cl

3 Sept 2026

Similarity-Aware Personalized Federated Learning in Heterogeneous Environments

arXiv:2609.02241v1 Announce Type: new Abstract: Federated Learning (FL) allows decentralized clients to train models collaboratively while preserving data privacy. However, distribution mismatch acros

3 Sept 2026

Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity

arXiv:2609.00632v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success across diverse domains, but their adaptation to privacy-sensitive, distributed datasets

researcharxiv-cs-ai

2 Sept 2026

EGT-KG: Evidence-Grounded Typed KG Retrieval for Practical Scientific QA with Small Language Models

arXiv:2609.00479v1 Announce Type: new Abstract: For emerging scientific research domains, local Small Language Models (SLMs) are becoming more attractive, as they offer stronger privacy control and mo

2 Sept 2026

Gaussian Core LoRA: Distribution-Aware Dynamic Adaptation for Broad Concept Erasure

arXiv:2609.01433v1 Announce Type: new Abstract: Concept erasure aims to suppress unsafe, privacy-sensitive, or undesirable generations in text-to-image diffusion models while preserving benign semanti

2 Sept 2026

An evidence-guided reinforcement learning method to improve psychiatric reasoning in small language models

arXiv:2602.06449v2 Announce Type: replace Abstract: Privacy and computational constraints limit the use of large language models in psychiatry, while adapting small language models (SLMs) often requir

safetyarxiv-cs-cl

Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

arXiv:2506.15626v3 Announce Type: replace-cross Abstract: extbf{Objective:} Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust Brai

researcharxiv-cs-ai

Applications

H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications

arXiv:2412.06210v3 Announce Type: replace Abstract: With the rapid development of the Internet of Things (IoT), federated learning (FL) has gained increasing attention for its privacy-preserving use o

applicationsarxiv-cs-lg

Agents

MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions

arXiv:2509.04183v3 Announce Type: replace-cross Abstract: The growing demand for scalable psychological counseling highlights the need for high-quality, privacy-compliant data, yet such data remains s

agentsarxiv-cs-ai

Multi-Scale Temporal Domain Alignment for Federated Video Domain Adaptation

arXiv:2608.29186v1 Announce Type: new Abstract: Federated Video Domain Adaptation (FVDA) enables collaborative learning across distributed and non-IID video datasets while preserving privacy, but is u

On the Plasticity Collapse in Continual Machine Unlearning

arXiv:2608.29513v1 Announce Type: cross Abstract: Machine unlearning enables deep neural networks to selectively remove the influence of specific data in response to privacy and regulatory requirement

Applications

Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning

arXiv:2608.29360v1 Announce Type: new Abstract: Spatiotemporal graphs underpin applications such as traffic forecasting, weather forecasting, and healthcare monitoring. Privacy regulations such as the

applicationsarxiv-cs-lg

How Far Can 5,500 Hours of Driving Take You? A Scaling Law Analysis of Video Diffusion Models

arXiv:2608.28404v1 Announce Type: new Abstract: Video generation for autonomous driving cannot follow the web-scale route: driving data is expensive to collect, bound by privacy requirements, and cann

31 Aug 2026

Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization

arXiv:2608.27791v1 Announce Type: new Abstract: Short-term load forecasting (STLF) provides essential information for numerous applications in modern power systems. However, accurate STLF often relies

31 Aug 2026

Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires

arXiv:2510.25384v2 Announce Type: replace Abstract: Large Language Models (LLMs) are promising tools for synthetic data generation in mental health. However, privacy policies and restrictions forced p

researcharxiv-cs-cl

31 Aug 2026

Federated Adversarial Training with Transformers

arXiv:2206.02131v2 Announce Type: replace-cross Abstract: Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the