Search: “privacy”

Efficient Secure Federated Learning via Information-Theoretically Secure Key Distribution: A Medical Imaging Case Study

arXiv:2610.06420v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative training of models across institutions without centralizing sensitive data, making it well-suited for priv

6 Oct 2026

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Hardware

FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing

DGX agent

arXiv:2508.12021v2 Announce Type: replace Abstract: Unsupervised federated learning (UFL) enables privacy-preserving distributed training without data labeling, yet practical deployment remains challe

hardwarearxiv-cs-lg

6 Oct 2026

RepTC: Representation-Aware Optimization for Efficient Traffic Classification on Edge IoT Devices

arXiv:2610.04784v1 Announce Type: cross Abstract: Traffic classification (TC) is crucial to secure Internet of Things (IoT) networks, whose edge nodes often operate under privacy, bandwidth, and energ

6 Oct 2026

VisualErase: Dual-Branch Visual Trajectory Redirection for Robust Concept Erasure in Text-to-Image Diffusion Models

arXiv:2610.05000v1 Announce Type: new Abstract: Concept erasure is essential for the safe deployment of text-to-image diffusion models, as they may reproduce harmful, copyrighted, or privacy-sensitive

researcharxiv-cs-cv

6 Oct 2026

Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation

arXiv:2610.03474v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches as

local-aiarxiv-cs-cv

5 Oct 2026

Applications

High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning

arXiv:2610.02578v1 Announce Type: cross Abstract: To commit to buying external data or participate in collaborative learning, one must decide whether the additional data will improve prediction enough

applicationsarxiv-cs-lg

5 Oct 2026

Safety

PoCoFL: POlicy-COmpliant Federated Learning

arXiv:2610.03650v1 Announce Type: cross Abstract: Federated Learning (FL) is a privacy-oriented learning paradigm that enables collaborative model training while keeping training data local to partici

safetyarxiv-cs-lg

5 Oct 2026

Continual Concept Erasure in Diffusion Models by Suppressing Cross-Edit Interference

arXiv:2610.01989v1 Announce Type: new Abstract: Concept erasure removes copyright-protected, privacy-sensitive, or otherwise undesirable concepts from pretrained text-to-image diffusion models to supp

researcharxiv-cs-cv

2 Oct 2026

FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantization

arXiv:2610.01537v1 Announce Type: new Abstract: Federated Learning (FL) enables privacy-preserving fine-tuning of Large Language Models (LLMs), yet the massive communication overhead remains a critica

2 Oct 2026

Interpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps

arXiv:2610.00391v1 Announce Type: cross Abstract: Synthetic medical tabular data generation has become essential for developing and validating computer-based medical systems (CBMSs) when real clinical

2 Oct 2026

Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning

arXiv:2609.39250v1 Announce Type: new Abstract: Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a c

1 Oct 2026

Applications

Taipan: A Query-free Transfer-based Multiple Sensitive Attribute Inference Attack Solely from Auxiliary Graphs

arXiv:2602.06700v2 Announce Type: replace-cross Abstract: Graph-structured data underpin a wide spectrum of modern applications, yet their multiple sensitive attributes are not isolated but deeply cou

applicationsarxiv-cs-lg

1 Oct 2026

Wavelet Flow Matching for Time Series

arXiv:2609.39374v1 Announce Type: cross Abstract: Synthetic time series are increasingly used for data augmentation, privacy-preserving data sharing, and downstream model development, yet faithfully r

1 Oct 2026

Meta-learning accelerates detector design optimization

arXiv:2609.35827v2 Announce Type: cross Abstract: The quality of a detector design is ultimately determined by the quality of the inference it enables, that is, by the accuracy with which the quantiti

OPFL: Optimistic Verification of Federated Learning via Empirical Boundary

arXiv:2609.37011v1 Announce Type: cross Abstract: Federated learning enables multiple clients to collaboratively train models without sharing their private data. However, the lack of visibility into l

PILLAR: Private Inverted-Index Lexical Lookup for Augmented Retrieval

arXiv:2609.36326v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) hands the user's query to whoever hosts the corpus. We propose PILLAR, a Privacy-Preserving RAG (PPRAG) system base

researcharxiv-cs-ai

Quantization Enables Private Dense Retrieval against Malicious Service Providers

arXiv:2609.36376v1 Announce Type: cross Abstract: Dense retrieval, the key component of Retrieval Augmented Generation (RAG), retrieves the most relevant documents by comparing dense vector representa

researcharxiv-cs-ai

Suppression Is Not Forgetting: Residual Recoverability in Visual Concept Unlearning for VLMs

arXiv:2604.03114v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) may need to forget visual concepts after deployment because of privacy, copyright, licensing, safety, or policy

TabKD: Tabular Knowledge Distillation through Interaction Diversity of Learned Feature Bins

arXiv:2603.15481v3 Announce Type: replace-cross Abstract: Data-free knowledge distillation enables model compression without original training data, critical for privacy-sensitive tabular domains. How

DBCF: Dual-Branch Complementary Fusion of Foundation Models for Generalized Deepfake Detection

model-releasesarxiv-cs-cv

arXiv:2609.34720v1 Announce Type: new Abstract: As image generation and editing technologies have progressed substantially, facial forgeries pose significant challenges to privacy and public safety. D

Digital Agriculture Sandbox for Collaborative Research

arXiv:2511.15990v2 Announce Type: replace-cross Abstract: Digital agriculture is transforming the way we grow food by utilizing technology to make farming more efficient, sustainable, and productive.

tutorialsarxiv-cs-lg

Models Designed to Forget: Machine Unlearning via Key Deletion

arXiv:2603.15033v4 Announce Type: replace Abstract: Machine unlearning for vision models is rapidly becoming a practical requirement, driven by privacy regulations, data errors, and the need to remove

OmniTide: Co-Designing Algorithms and Systems for Efficient On-Device Omni-LLM Streaming

arXiv:2609.34653v1 Announce Type: cross Abstract: On-device streaming omni-modal inference safeguards user privacy and eliminates prohibitive per-token API costs, but faces a critical bottleneck: the

Synthetic data for ratemaking: imputation-based methods vs adversarial networks and autoencoders

arXiv:2509.02171v3 Announce Type: replace-cross Abstract: Actuarial ratemaking depends on high-quality data, yet access to such data is often limited by the cost of obtaining new data, privacy concern

Distributed Learning as a Service: The Developer's Perspective

arXiv:2609.31061v1 Announce Type: new Abstract: Application developers of distributed learning services face challenges that a typical federated learning loop does not address. Specifically, the model

Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networks

arXiv:2609.30581v1 Announce Type: cross Abstract: Encrypted training relies on keeping server-side updates low-degree. This constraint traditionally excludes models whose weights inhabit a compact Lie

Energy Efficiency of Locally Deployed LLMs: A Preliminary Quantitative GPU Power Benchmark on Consumer Hardware

arXiv:2608.00008v2 Announce Type: replace Abstract: The local deployment of large language models (LLMs) is gaining traction due to privacy concerns and the desire for on-premise inference. However, t

Moment-guided edge sampling

arXiv:2609.30472v1 Announce Type: new Abstract: Edge sampling makes local decisions to achieve graph-level objectives, such as preserving structural properties. This creates a fundamental challenge: e

Neuralyzing the Trace: Selective Representation-Level Unlearning with Contrastive Sparse Autoencoders

arXiv:2609.31056v1 Announce Type: new Abstract: Machine unlearning aims to remove targeted information while preserving a model's other abilities. In realistic settings, such as privacy requests under

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

Applications

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

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

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

model-releasesarxiv-cs-cv

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

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

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

Safety

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

Safety

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