Search: “privacy”

Throat and acoustic paired speech dataset for deep learning-based speech enhancement

arXiv:2502.11478v3 Announce Type: replace-cross Abstract: In high-noise environments such as factories, subways, and busy streets, capturing clear speech is challenging. Throat microphones can offer a

23 Apr 2026

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Verification of Machine Unlearning is Fragile

DGX agent

arXiv:2408.00929v2 Announce Type: replace Abstract: As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data fr

safetyarxiv-cs-lg

23 Apr 2026

Agents

Rethinking Scale: Deployment Trade-offs of Small Language Models under Agent Paradigms

arXiv:2604.19299v1 Announce Type: cross Abstract: Despite the impressive capabilities of large language models, their substantial computational costs, latency, and privacy risks hinder their widesprea

agentsarxiv-cs-ai

22 Apr 2026

Robust Continual Unlearning against Knowledge Erosion and Forgetting Reversal

arXiv:2604.19108v1 Announce Type: cross Abstract: As a means to balance the growth of the AI industry with the need for privacy protection, machine unlearning plays a crucial role in realizing the ``r

22 Apr 2026

A Model and Estimation of the Bitcoin Transaction Fee

arXiv:2604.17183v1 Announce Type: cross Abstract: Bitcoin transaction fees will become more important as the block subsidy declines, but fee formation is hard to study with blockchain data alone becau

Fairness Constraints in High-Dimensional Generalized Linear Models

arXiv:2604.16610v1 Announce Type: cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability. Conventional fairness

Applications

Federated Rule Ensemble Method in Medical Data

arXiv:2604.17956v1 Announce Type: new Abstract: Machine learning has become integral to medical research and is increasingly applied in clinical settings to support diagnosis and decision-making; howe

applicationsarxiv-cs-lg

Agents

Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework

arXiv:2511.21686v2 Announce Type: replace Abstract: Synthetic data has become increasingly important for training large language models, especially when real data is scarce, expensive, or privacy-sens

agentsarxiv-cs-cl

Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis

model-releasesarxiv-cs-cl

arXiv:2602.16144v3 Announce Type: replace Abstract: As multimodal systems increasingly process sensitive personal data, the ability to selectively revoke specific data modalities has become a critical

Applications

NullFace: Training-Free Localized Face Anonymization

arXiv:2503.08478v2 Announce Type: replace Abstract: Privacy concerns around ever increasing number of cameras are increasing in today's digital age. Although existing anonymization methods are able to

applicationsarxiv-cs-cv

Towards a Data-Parameter Correspondence for LLMs: A Preliminary Discussion

model-releasesarxiv-cs-lg

arXiv:2604.17384v1 Announce Type: new Abstract: Large language model optimization has historically bifurcated into isolated data-centric and model-centric paradigms: the former manipulates involved sa

CiPO: Counterfactual Unlearning for Large Reasoning Models through Iterative Preference Optimization

arXiv:2604.15847v1 Announce Type: new Abstract: Machine unlearning has gained increasing attention in recent years, as a promising technique to selectively remove unwanted privacy or copyrighted infor

researcharxiv-cs-cl

20 Apr 2026

EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond

arXiv:2411.18328v2 Announce Type: replace Abstract: Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with tradition

20 Apr 2026

Synthetic data in cryptocurrencies using generative models

arXiv:2604.16182v1 Announce Type: cross Abstract: Data plays a fundamental role in consolidating markets, services, and products in the digital financial ecosystem. However, the use of real data, espe

researcharxiv-cs-ai

20 Apr 2026

Asynchronous Probability Ensembling for Federated Disaster Detection

arXiv:2604.14450v1 Announce Type: new Abstract: Quick and accurate emergency handling in Disaster Decision Support Systems (DDSS) is often hampered by network latency and suboptimal application accura

Bias in Surface Electromyography Features across a Demographically Diverse Cohort

arXiv:2604.14460v1 Announce Type: cross Abstract: Neuromotor decoding from upper-limb electromyography (sEMG) can enhance human-machine interfaces and offer a more natural means of controlling prosthe

Tutorials

Differentially Private Conformal Prediction

arXiv:2604.14621v1 Announce Type: cross Abstract: Conformal prediction (CP) has attracted broad attention as a simple and flexible framework for uncertainty quantification through prediction sets. In

tutorialsarxiv-cs-lg

Domain-Adaptive Model Merging Across Disconnected Modes

arXiv:2603.05957v2 Announce Type: replace-cross Abstract: Learning across domains is challenging when data cannot be centralized due to privacy or heterogeneity, which limits the ability to train a si

researcharxiv-cs-ai

VoxSafeBench: Not Just What Is Said, but Who, How, and Where

arXiv:2604.14548v1 Announce Type: cross Abstract: As speech language models (SLMs) transition from personal devices into shared, multi-user environments, their responses must account for far more than

From Anchors to Supervision: Memory-Graph Guided Corpus-Free Unlearning for Large Language Models

arXiv:2604.13777v1 Announce Type: new Abstract: Large language models (LLMs) may memorize sensitive or copyrighted content, raising significant privacy and legal concerns. While machine unlearning has

local-aiarxiv-cs-cl

16 Apr 2026

IMU: Influence-guided Machine Unlearning

arXiv:2508.01620v3 Announce Type: replace-cross Abstract: Machine Unlearning (MU) aims to selectively erase the influence of specific data points from pretrained models. However, most existing MU meth

Probabilistic Feature Imputation and Uncertainty-Aware Multimodal Federated Aggregation

arXiv:2604.12970v1 Announce Type: cross Abstract: Multimodal federated learning enables privacy-preserving collaborative model training across healthcare institutions. However, a fundamental challenge

safetyarxiv-cs-cv

Safe-FedLLM: Delving into the Safety of Federated Large Language Models

arXiv:2601.07177v2 Announce Type: replace-cross Abstract: Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs). Most prior work focuses on imp

local-aiarxiv-cs-ai

Tutorials

SDFed: Bridging Local Global Discrepancy via Subspace Refinement and Divergence Control in Federated Prompt Learning

arXiv:2602.08590v4 Announce Type: replace Abstract: Vision-language pretrained models offer strong transferable representations, yet adapting them in privacy-sensitive multi-party settings is challeng

tutorialsarxiv-cs-lg

Towards Generalized Certified Robustness with Multi-Norm Training

arXiv:2410.03000v3 Announce Type: replace Abstract: Existing certified training methods can only train models to be robust against a certain perturbation type (e.g. l_infty or l_2). However, an l_inft

FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification

model-releasesarxiv-cs-ai

arXiv:2508.17431v3 Announce Type: replace-cross Abstract: Person re-identification (re-ID) is a fundamental task in intelligent surveillance and public safety. Federated learning (FL) provides a priva

Generating High Quality Synthetic Data for Dutch Medical Conversations

arXiv:2604.09645v1 Announce Type: cross Abstract: Medical conversations offer insights into clinical communication often absent from Electronic Health Records. However, developing reliable clinical Na

researcharxiv-cs-ai

Poisoning with A Pill: Circumventing Detection in Federated Learning

arXiv:2407.15389v2 Announce Type: replace Abstract: Without direct access to the client's data, federated learning (FL) is well-known for its unique strength in data privacy protection among existing

Sense Less, Infer More: Agentic Multimodal Transformers for Edge Medical Intelligence

arXiv:2604.10404v1 Announce Type: cross Abstract: Edge-based multimodal medical monitoring requires models that balance diagnostic accuracy with severe energy constraints. Continuous acquisition of EC

From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detection

arXiv:2604.09327v1 Announce Type: new Abstract: Pose-based Video Anomaly Detection (VAD) has gained significant attention for its privacy-preserving nature and robustness to environmental variations.

local-aiarxiv-cs-cv

Agents

Multi-User Large Language Model Agents

arXiv:2604.08567v1 Announce Type: new Abstract: Large language models (LLMs) and LLM-based agents are increasingly deployed as assistants in planning and decision making, yet most existing systems are

agentsarxiv-cs-cl

On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs

model-releasesarxiv-cs-ai

arXiv:2509.25214v3 Announce Type: replace-cross Abstract: As increasingly large pre-trained models are released, deploying them on edge devices for privacy-preserving applications requires effective c

Pretrain-then-Adapt: Uncertainty-Aware Test-Time Adaptation for Text-based Person Search

model-releasesarxiv-cs-cv

arXiv:2604.08598v1 Announce Type: cross Abstract: Text-based person search faces inherent limitations due to data scarcity, driven by stringent privacy constraints and the high cost of manual annotati

RansomTrack: A Hybrid Behavioral Analysis Framework for Ransomware Detection

model-releasesarxiv-cs-lg

arXiv:2604.08739v1 Announce Type: cross Abstract: Ransomware poses a serious and fast-acting threat to critical systems, often encrypting files within seconds of execution. Research indicates that ran

Bias Redistribution in Visual Machine Unlearning: Does Forgetting One Group Harm Another?

arXiv:2604.08111v1 Announce Type: cross Abstract: Machine unlearning enables models to selectively forget training data, driven by privacy regulations such as GDPR and CCPA. However, its fairness impl

safetyarxiv-cs-cv

Tutorials

How to sketch a learning algorithm

arXiv:2604.07328v1 Announce Type: new Abstract: How does the choice of training data influence an AI model? This question is of central importance to interpretability, privacy, and basic science. At i

tutorialsarxiv-cs-lg

LLM-based Schema-Guided Extraction and Validation of Missing-Person Intelligence from Heterogeneous Data Sources

arXiv:2604.06571v1 Announce Type: cross Abstract: Missing-person and child-safety investigations rely on heterogeneous case documents, including structured forms, bulletin-style posters, and narrative

safetyarxiv-cs-ai

ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference

arXiv:2508.16703v4 Announce Type: replace-cross Abstract: On-device running Large Language Models (LLMs) is nowadays a critical enabler towards preserving user privacy. We observe that the attention o

local-aiarxiv-cs-ai

Towards the Development of an LLM-Based Methodology for Automated Security Profiling in Compliance with Ukrainian Cybersecurity Regulations

arXiv:2604.06274v1 Announce Type: cross Abstract: In recent years, the pace of development of information technology in various areas has increased drastically, forcing cybersecurity specialists to co

safetyarxiv-cs-ai

Applications

Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions

arXiv:2610.15091v1 Announce Type: cross Abstract: Argument structures represent how individual arguments interact within a discourse. However, leveraging these structures to infer meaningful knowledge

applicationsarxiv-cs-ai

Learning Visual Speech Representations via Cross-Modal Distillation and Joint Face-Lip Modeling

model-releasesarxiv-cs-cv

arXiv:2610.19559v1 Announce Type: new Abstract: Lipreading remains challenging due to the inherent ambiguity of visual speech cues and their sensitivity to head pose and video resolution. In this work

Multimodal Medical Image Parsing with Task-Specific Low-Rank Adapters

arXiv:2610.19901v1 Announce Type: new Abstract: Adapting a single vision-language model to medical image parsing requires training choices that reflect the demands of each task. We study task-specific

No Way To Steal My Face: Proactive Defense Against Identity-Preserving Personalized Generation

arXiv:2610.19717v1 Announce Type: new Abstract: Recent advances in diffusion models have enabled high-fidelity, identity-preserving image generation for personalized applications such as digital avata

Pre-Trained Language Models as Zero-Shot Tools for Social Science Research

arXiv:2610.19048v1 Announce Type: new Abstract: Record linkage and demographic inference from names are routine demands in social science. Most approaches to these problems require labeled training da

safetyarxiv-cs-cl

Quantifying Annotation Agreement in Object Detection with Optimal Transport

arXiv:2610.19912v1 Announce Type: new Abstract: Reliable validation of machine learning models is a crucial step in designing robust and trustworthy systems. Yet validation relies on annotated test se

Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion

arXiv:2610.19494v1 Announce Type: new Abstract: Real-time monocular human pose estimation is an important sensing component for human-centered intelligent systems. While recent camera-based methods ha

Should SLMs be in the Driver's Seat? A Study of Local Models as Interpreters in Cloud-Written Agent Programs

arXiv:2610.14209v1 Announce Type: new Abstract: The most accurate agents executing user-provided tasks are driven by large, cloud-hosted models, which solve them by reading the private data of the use

local-aiarxiv-cs-ai

Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction

arXiv:2610.19640v1 Announce Type: new Abstract: The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors. We present enviGraph, a framework that gene