Search: “privacy”

arXiv:2607.11975v1 Announce Type: cross Abstract: Current machine unlearning methods predominantly rely on global, coarse-grained intervention strategies. They lack precise pilot signals to guide the

tutorialsarxiv-cs-ai

15 Jul 2026

Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces

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DGX agent

arXiv:2503.15414v3 Announce Type: replace-cross Abstract: Federated learning seeks to foster collaboration among distributed clients while preserving the privacy of their local data. Traditional feder

local-aiarxiv-cs-cv

10 Jul 2026

When Synthetic Speech Is All You Have: Better Call GRPO

arXiv:2607.08409v1 Announce Type: cross Abstract: LLM-based ASR adapted to regulated domains such as banking is bottlenecked by privacy: real speech is costly and legally constrained to collect, makin

safetyarxiv-cs-ai

10 Jul 2026

FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation

arXiv:2607.07314v1 Announce Type: cross Abstract: Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the l

Hardware

Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization

arXiv:2607.06922v1 Announce Type: cross Abstract: Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding t

hardwarearxiv-cs-cv

POPS: Recovering Unlearned Multi-Modality Knowledge in MLLMs with Prompt-Optimized Parameter Shaking

arXiv:2607.06649v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive performance on cross-modal tasks by jointly training on large-scale textual and

Synthetic Time Series Generation via Complex Networks

arXiv:2601.22879v2 Announce Type: replace Abstract: Time series data are essential for a wide range of applications, yet access to high-quality datasets is often constrained by privacy concerns, acqui

applicationsarxiv-cs-lg

What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale

arXiv:2510.13817v2 Announce Type: replace Abstract: The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountabili

CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming

arXiv:2607.05571v1 Announce Type: new Abstract: Large language models are increasingly explored as AI tutors, yet deploying them in K-12 settings raises concerns around privacy, cost, and reliance on

Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure

arXiv:2603.10254v2 Announce Type: replace Abstract: Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains. Tabular Prior-Data Fitted Network (TabPFN

Is Your NPU Ready for LLMs? Dissecting the Hidden Efficiency Bottlenecks in Mobile LLM Inference

arXiv:2607.05475v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) on mobile devices enhances privacy and reduces latency, but is severely bottlenecked by hardware inefficiency.

TILDE: TILt-based Distributional Erasure for Concept Unlearning

arXiv:2607.06432v1 Announce Type: cross Abstract: Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes,

safetyarxiv-cs-ai

Channel-Adaptive Robust Aggregation for Over-the-Air Federated Learning in Heterogeneous Networks

arXiv:2607.04218v1 Announce Type: new Abstract: The growing demand for privacy-preserving, data-intensive applications such as IoT, augmented reality, and autonomous systems positions Federated Learni

safetyarxiv-cs-lg

NouveauVoice: Generating Novel Pseudo Speakers for Voice Anonymization

arXiv:2607.03985v1 Announce Type: cross Abstract: Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Spea

One Framework for All: Cross-Modal Membership Inference for Generative Models

arXiv:2607.04339v1 Announce Type: cross Abstract: Large generative models across text-to-text, text-to-image, and image-to-text modalities have been shown to pose significant privacy risks. One fundam

applicationsarxiv-cs-ai

OpenGlass: A Sensing-Computing Split Architecture for Local MLLM-Driven Real-Time Visual Assistance

arXiv:2607.03213v1 Announce Type: cross Abstract: We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind

local-aiarxiv-cs-ai

pFedNavi: Structure-Aware Personalized Federated Vision-Language Navigation for Embodied AI

arXiv:2602.14401v2 Announce Type: replace-cross Abstract: Vision-Language Navigation VLN requires large-scale trajectory instruction data from private indoor environments, raising significant privacy

PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement

arXiv:2607.02897v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong capabilities, but they may memorize private information from web data, raising privacy conc

RePos: Relative-to-Absolute Output Factorization for Cross-Environment WiFi-Based 3D Human Pose Estimation

arXiv:2607.02986v1 Announce Type: new Abstract: Device-free 3D human pose estimation using commodity WiFi Channel State Information (CSI) enables privacy-preserving and illumination-robust human sensi

local-aiarxiv-cs-cv

TrustCLIP: Learning Private Visual Features via Adversarial Reconstruction

arXiv:2607.04484v1 Announce Type: new Abstract: Vision and vision-language models rely on high-level visual representations that are increasingly used across recognition, retrieval, and multimodal rea

researcharxiv-cs-cv

Locality-Aware Continual Unlearning for Diffusion Models

arXiv:2512.02657v2 Announce Type: replace-cross Abstract: Real-world deployment of text-to-image diffusion models requires continual concept removal as new privacy, copyright, or safety obligations ar

3 Jul 2026

QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition

arXiv:2607.02426v1 Announce Type: cross Abstract: Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, making it suitable for privacy-sensi

3 Jul 2026

From 'Strings' to 'Things' for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems

arXiv:2607.00003v1 Announce Type: cross Abstract: Personal Knowledge Graphs (PKGs) offer a privacy-preserving framework for modeling user preferences, yet constructing them from unstructured, decentra

2 Jul 2026

LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data

arXiv:2607.00733v1 Announce Type: cross Abstract: Mechanistic modeling via ordinary differential equations (ODEs) provides interpretable descriptions of complex dynamics and enables inference of under

2 Jul 2026

Amplifying Membership Signal Through Chained Regeneration

arXiv:2606.31991v1 Announce Type: cross Abstract: The tendency of large generative models to memorize training data makes sample verification critical for privacy auditing and copyright enforcement. C

FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning

arXiv:2606.31742v1 Announce Type: cross Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep lear

PA-VAD: Diffusion-Based Pseudo-Only Video Anomaly Detection via Domain-Aligned Memory Updates

arXiv:2512.06845v2 Announce Type: replace Abstract: Deploying video anomaly detection (VAD) in the real world is often constrained by the scarcity, privacy, and cost of collecting real abnormal footag

safetyarxiv-cs-cv

Private Rate-Constrained Optimization with Applications to Fair Learning

arXiv:2505.22703v2 Announce Type: replace Abstract: Many problems in trustworthy ML can be expressed as constraints on prediction rates across subpopulations, including group fairness constraints (dem

How to Leverage Synthetic Speech for LLM-Based ASR Systems?

arXiv:2606.29031v1 Announce Type: cross Abstract: In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from mo

local-aiarxiv-cs-ai

muFlow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors

arXiv:2606.30528v1 Announce Type: new Abstract: Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy a

safetyarxiv-cs-cv

The Forgetting-Retention Dilemma: Certified Unlearning Theory in Continual Learning

arXiv:2606.29832v1 Announce Type: new Abstract: Machine unlearning aims to eliminate the influence of specific data from trained models to safeguard privacy. However, this presents a significant chall

researcharxiv-cs-lg

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks

arXiv:2504.17421v2 Announce Type: replace-cross Abstract: Large language models (LMs) offer broad generalization capabilities but require vast amounts of data and computational resources for domain-sp

AnySimLite: A Lightweight Few-Shot Similarity Encoder for On-Device Speech-Adjacent Classification

arXiv:2606.26452v1 Announce Type: new Abstract: To minimize privacy concerns and inference latency on edge devices like smartphones, lightweight on-device models remain important for end-user applicat

local-aiarxiv-cs-cl

26 Jun 2026

Byzantine-Robust Aggregation for Securing Decentralized Federated Learning

arXiv:2409.17754v2 Announce Type: replace-cross Abstract: Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on de

local-aiarxiv-cs-ai

26 Jun 2026

Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets

model-releasesarxiv-cs-cv

arXiv:2603.24991v2 Announce Type: replace Abstract: Event-based vision, characterized by low redundancy, focus on dynamic motion, and inherent privacy-preserving properties, naturally fits the demands

26 Jun 2026

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

arXiv:2510.04773v2 Announce Type: replace Abstract: As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiv

25 Jun 2026

FedReLa: Imbalanced Federated Learning via Re-Labeling

arXiv:2606.26037v1 Announce Type: cross Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation. The global class imbalance and cro

local-aiarxiv-cs-cv

25 Jun 2026

Transferable Attack against Face Swapping in an Extended Space

arXiv:2606.25376v1 Announce Type: new Abstract: Although deep Face Swapping (FS) models may benefit the entertainment industry, they pose severe threats to privacy and security. Existing protections,

researcharxiv-cs-cv

25 Jun 2026

A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning

arXiv:2307.13127v3 Announce Type: replace-cross Abstract: Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information. While differential pr

applicationsarxiv-cs-lg

EnerInfer: Energy-Aware On-Device LLM Inference

arXiv:2606.23001v1 Announce Type: cross Abstract: On-device LLM inference is increasingly attractive for privacy-preserving, reliable, and cost-effective deployment, yet its energy and thermal costs r

local-aiarxiv-cs-lg

Federated Survival Analysis in Healthcare: A Multi-Model Evaluation on Cross-Institutional Heterogeneous Breast Cancer Data

arXiv:2606.23871v1 Announce Type: new Abstract: Survival analysis is central to clinical decision-making, yet reliable time-to-event models require large, diverse cohorts that are rarely available at

local-aiarxiv-cs-lg

LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings

arXiv:2602.18934v2 Announce Type: replace Abstract: Membership inference attacks (MIAs) threaten the privacy of machine learning models by revealing whether a specific data point was used during train

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

arXiv:2606.17110v2 Announce Type: replace-cross Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user convers

local-aiarxiv-cs-lg

Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach

arXiv:2606.24237v1 Announce Type: new Abstract: Federated Graph Learning facilitates collaborative graph modeling across distributed clients while preserving data privacy. However, real-world data cat

applicationsarxiv-cs-ai

FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation

arXiv:2606.23370v1 Announce Type: cross Abstract: Device-side Large Language Models (LLMs) have grown explosively, offering stronger privacy and higher availability than their cloud-side counterparts.

researcharxiv-cs-lg

GRAG: Generic Response-Augmented Generation Framework for Personalized Conversational Systems

arXiv:2606.21097v1 Announce Type: cross Abstract: Deploying highly capable personalized conversational agents in resource-constrained or privacy-sensitive environments remains a significant challenge.

Model Inversion meets Cryptographic Fuzzy Extractors

arXiv:2510.25687v3 Announce Type: replace-cross Abstract: Model inversion attacks pose an open challenge to privacy-sensitive applications that use machine learning (ML) models. For example, face auth

researcharxiv-cs-lg

Hardware

SCENIC: Semantic-Conditioned Edge-Aware Neural Framework for Structured IoT Command Generation

arXiv:2606.22296v1 Announce Type: new Abstract: Edge Internet of Things (IoT) agents are often constrained by memory capacity, privacy requirements, communication latency, and recurring inference cost

hardwarearxiv-cs-lg