Search: “privacy”

EgoInertia-MI: A Multimodal Egocentric Vision and IMU Benchmark for Motor Impairment Assessment

model-releasesarxiv-cs-cv

arXiv:2607.03934v1 Announce Type: new Abstract: Motor impairments, including tremor, bradykinesia, gait abnormalities, and postural instability, are common across many neurological and movement-relate

7 Jul 2026

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Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data

DGX agent

arXiv:2607.02636v1 Announce Type: cross Abstract: Object detection is a fundamental capability for AI-driven perception in safety-critical drone and edge-vision systems, including disaster response, o

local-aiarxiv-cs-ai

7 Jul 2026

QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting

arXiv:2607.02632v1 Announce Type: cross Abstract: Time-series forecasting supports decisions in finance, en-ergy, transportation, public health, and industrial monitoring. Recent foundation models imp

applicationsarxiv-cs-ai

7 Jul 2026

Routing Anonymity and Identifiability of Noisy Quantum Hardware

arXiv:2607.05281v1 Announce Type: cross Abstract: Present-day quantum computing is cloud-based, where a user submits a circuit to a service provider's proprietary backend hardware. While providers may

7 Jul 2026

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey

arXiv:2607.01305v1 Announce Type: cross Abstract: Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Interne

3 Jul 2026

A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems

arXiv:2606.31639v1 Announce Type: cross Abstract: Large language models are no longer only text generators. They are increasingly embedded in retrieval pipelines, enterprise assistants, coding environ

1 Jul 2026

Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation

model-releasesarxiv-cs-cv

arXiv:2606.20196v2 Announce Type: replace Abstract: Continual Test-Time Adaptation (CTTA) aims to maintain model performance under evolving target domains by adapting online without labeled data. Howe

1 Jul 2026

ELEVATE: Designing Human-Centered GenAI Virtual Tutors for Scalable and Inclusive Education

arXiv:2606.30662v1 Announce Type: cross Abstract: The advent of Generative Artificial Intelligence (GenAI), and in particular Large Language Models (LLMs), is reshaping educational practice, while int

local-aiarxiv-cs-ai

1 Jul 2026

Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Models

arXiv:2606.29897v1 Announce Type: cross Abstract: Voice anonymization aims to protect speaker identity while preserving linguistic content and speech usability. However, most anonymization systems are

researcharxiv-cs-ai

30 Jun 2026

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models

arXiv:2606.29748v1 Announce Type: new Abstract: The application of graph data in numerous disciplines raises the need for gathering and analyzing huge volumes of data, some of which is private and sen

30 Jun 2026

You can embed this model practically anywhere - like a chrome extension This is transformersjs + rampart for real-time PII removal in your b…

applicationsclem-delangue--x

You can embed this model practically anywhere - like a chrome extension This is transformersjs + rampart for real-time PII removal in your browser (personal email still blurred) You can see I'm toggli

30 Jun 2026

CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection

arXiv:2504.01882v2 Announce Type: replace Abstract: The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network s

local-aiarxiv-cs-lg

29 Jun 2026

Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT

arXiv:2601.01701v2 Announce Type: replace-cross Abstract: Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems. Recently, wi

26 Jun 2026

Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork

model-releasesarxiv-cs-lg

arXiv:2606.26772v1 Announce Type: new Abstract: Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD

26 Jun 2026

Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models

arXiv:2504.09910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sen

researcharxiv-cs-cl

25 Jun 2026

A Survey on Federated Causal Discovery and Inference

arXiv:2606.23741v1 Announce Type: cross Abstract: Causal reasoning, which encompasses the discovery of causal structures and the inference of causal effects, is fundamental to data-driven decision mak

researcharxiv-cs-ai

24 Jun 2026

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training

arXiv:2507.01752v4 Announce Type: replace-cross Abstract: Gradient-based optimization is the workhorse of deep learning, offering efficient and scalable training via backpropagation. However, exposing

24 Jun 2026

EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors

arXiv:2602.21218v2 Announce Type: replace-cross Abstract: High-quality data is essential for modern machine learning, yet many valuable corpora are sensitive and cannot be freely shared. Synthetic dat

Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models

arXiv:2606.22618v1 Announce Type: new Abstract: Climate change, primarily driven by carbon dioxide (CO2) emissions, requires accurate forecasting tools to support effective mitigation policies and sus

Federated Temporal Attention Intelligence for Cyber-Resilient IoMT: Lightweight Digital Twins and PPO-Driven Honeypot Deception

arXiv:2606.21422v1 Announce Type: new Abstract: The rapid proliferation of Internet of Medical Things (IoMT) devices introduces critical cybersecurity vulnerabilities in healthcare environments where

Good-Enough LLM Obfuscation (GELO)

model-releasesarxiv-cs-lg

arXiv:2603.05035v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly served on shared accelerators where an adversary with read access to device memory can observe K

MoE Enhanced Federated Learning for Spatiotemporal Prediction

arXiv:2606.10499v1 Announce Type: cross Abstract: Traffic prediction is fundamental to intelligent transportation systems and urban computing, yet many cities continue to suffer from traffic data scar

10 Jun 2026

Differentially Private Range Subgraph Counting

arXiv:2606.08179v1 Announce Type: cross Abstract: Subgraph counting is a fundamental problem in graph analysis. Motivated by practical scenarios where graph analytics are performed on subgraphs induce

9 Jun 2026

Disjoint Generation of Synthetic Data

arXiv:2507.19700v2 Announce Type: replace Abstract: We propose a new framework for generating tabular synthetic datasets via disjoint generative models. In this paradigm, a dataset is partitioned into

9 Jun 2026

Uncertainty-Aware Hierarchical Re-Localization in OpenStreetMap via Semantic Alignment

arXiv:2603.01613v2 Announce Type: replace Abstract: Monocular re-localization enables robots to estimate camera poses from visual observations. However, many existing methods rely on dense maps or lar

safetyarxiv-cs-cv

9 Jun 2026

AIDEN: Design and Pilot Study of an AI Assistant for the Visually Impaired

arXiv:2511.06080v4 Announce Type: replace Abstract: This paper presents AIDEN, an artificial intelligence-based assistant designed to enhance the autonomy and daily quality of life of visually impaire

researcharxiv-cs-cv

8 Jun 2026

Synapse: Federated Tool Routing via Typed Compendium Artifacts

arXiv:2602.00911v2 Announce Type: replace Abstract: The unit of collaboration in federated learning determines what guarantees are even expressible. Flat units like weights, prompts, raw examples, car

6 Jun 2026

Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use

model-releasesarxiv-cs-cl

arXiv:2603.03205v2 Announce Type: replace Abstract: Agentic language models operate in a fundamentally different safety regime than chat models: they must plan, call tools, and execute long-horizon ac

4 Jun 2026

FederatedSkill: Federated Learning for Agentic Skill Evolution

arXiv:2606.03143v1 Announce Type: cross Abstract: Modern LLM agents increasingly rely on skill libraries to handle complex tasks, making skill evolution a primary driver of self-improvement. However,

local-aiarxiv-cs-cl

FGRPO: Federated GRPO with Adaptive Aggregation on Non-IID Data

arXiv:2606.03094v1 Announce Type: new Abstract: Recent advances in language models have established reinforcement learning as the primary paradigm for eliciting self-correction and long-chain reasonin

Hierarchical Federated Learning with Dynamic Clustering and Adaptive Regularization for Robust Infrastructure Inspection

arXiv:2606.03084v1 Announce Type: new Abstract: The deployment of data-driven computer vision models for structural health monitoring (SHM) is heavily constrained by the data silo dilemma due to strin

applicationsarxiv-cs-cv

WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition

arXiv:2606.02974v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems,

applicationsarxiv-cs-ai

Canonicalized Stable-List Replay for Private Federated Continual Learning over Language-Model Embeddings

arXiv:2606.00426v1 Announce Type: new Abstract: Federated continual learning (FCL) lets distributed clients adapt language-model heads to evolving NLP tasks without sharing raw text. Under user-level

Cross-modal linkage risk in clinical vision-language models

arXiv:2606.02276v1 Announce Type: cross Abstract: Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shared embedding space that can preserve instance-leve

Fair Finetuning Mitigates Distribution Inference Attacks

arXiv:2606.01719v1 Announce Type: cross Abstract: Machine learning models trained on sensitive data can inadvertently leak population-level information about their training distributions -- a threat k

Keeping sensitive data on device while cloud AI adds scale and context, @perplexity_ai @AravSrinivas demonstrates hybrid local server infere…

Perplexity AI demonstrates a hybrid approach to AI inference that combines on-device processing for sensitive data with cloud-based AI services to achieve scalability and contextual understanding. Thi

local-aiperplexity--x

Agents

Multi-Agent Conformal Prediction with Personalized Statistical Validity

arXiv:2606.00717v1 Announce Type: cross Abstract: Uncertainty quantification is essential in high-stakes machine learning tasks. However, one of the principled solutions, conformal prediction, faces c

agentsarxiv-cs-ai

Causal Evaluation of Membership Inference Attacks

arXiv:2602.02819v3 Announce Type: replace Abstract: Membership Inference Attacks (MIAs) aim to distinguish training points (members) from unseen data (non-members), and are widely used to quantify mem

DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs

arXiv:2605.31427v1 Announce Type: new Abstract: Dynamic graph learning (DGL) is essential for modelling evolving graph data, but existing methods suffer from significant computational overhead due to

Regret-Based Federated Causal Discovery with Unknown Interventions

arXiv:2512.23626v2 Announce Type: replace Abstract: Most causal discovery methods recover a completed partially directed acyclic graph representing a Markov equivalence class from observational data.

researcharxiv-cs-ai

Translation Analytics for Freelancers II: Benchmarking Local LLMs for Confidential Translation Workflows

model-releasesarxiv-cs-cl

arXiv:2605.31452v1 Announce Type: new Abstract: Building on our previous work, this paper develops practical, low-barrier methods for freelance translators and smaller language service providers to ev

Ciphera: A Decentralised Biometric Identity Framework

arXiv:2605.29868v1 Announce Type: cross Abstract: Centralised biometric identity systems expose users to single points of failure, opaque verification processes, and irreversible biometric compromise.

applicationsarxiv-cs-cv

FPLIER: Federated Pathway-Level Information Extractor

model-releasesarxiv-cs-lg

arXiv:2605.29587v1 Announce Type: cross Abstract: In transcriptomics, gene-set-aware factorization methods such as the Pathway Level Information Extractor (PLIER) are most effective when trained on la

LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback

arXiv:2605.30273v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in generating supportive responses for mental health queries, but improving their usefulness, empathy, and s

Optimal Rates for Differentially Private Hypothesis Testing with E-values

arXiv:2605.28952v1 Announce Type: cross Abstract: E-values have attracted considerable interest in recent years as flexible tools for enabling anytime-valid and adaptive data analysis. Hypothesis test

Tutorials

Robust Cross-Domain Generalization Using Unlabeled Target Data with Source-Domain Supervision

arXiv:2605.29122v1 Announce Type: new Abstract: It is often desirable to generalize medical imaging AI models trained with dense annotations to data acquired from different ultrasound scanners or clin

tutorialsarxiv-cs-cv

Statistical Embeddings for Similarity, Retrieval, and Interpretable Alignment of Numeric Tabular Datasets

arXiv:2605.30289v1 Announce Type: new Abstract: Numeric tabular datasets are the dominant data format in scientific practice, yet large language models lack native mechanisms for representing numeric

Tutorials

Adaptive Sampling and Clipping for Private Worst-Case Group Optimization