Search: “privacy”

arXiv:2302.05545v4 Announce Type: replace-cross Abstract: A novel form of inference attack in vertical federated learning (VFL) is proposed, where two parties collaborate in training a machine learnin

agentsarxiv-cs-lg

14 Apr 2026

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A Sharp Transition in Data Reconstruction under Differential Privacy

DGX agent

arXiv:2609.37344v1 Announce Type: cross Abstract: Data reconstruction attacks have empirically been successful in recovering training samples from learned models, raising privacy concerns and motivati

researcharxiv-cs-ai

30 Sept 2026

From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification

model-releasesarxiv-cs-cv

arXiv:2503.12232v3 Announce Type: replace Abstract: Aiming to match pedestrian images captured under varying lighting conditions, visible-infrared person re-identification (VI-ReID) has drawn intensiv

30 Sept 2026

Where Privacy Belongs: Placement Diagnosis and Certified Selection for Private Counterfactual Explanations on Graphs

arXiv:2609.37667v1 Announce Type: new Abstract: Counterfactual explanations for graph neural networks (GNNs) find the minimal intervention that flips a node's prediction--but computing one requires re

30 Sept 2026

mmHRI: Towards Privacy-Preserving Human-Robot Interaction with Millimeter-Wave Radar

arXiv:2609.34220v1 Announce Type: cross Abstract: Assistive robots increasingly operate in many human-centered environments and perform various human-robot interaction (HRI) tasks, such as object deli

safetyarxiv-cs-cv

29 Sept 2026

Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores

arXiv:2609.29453v1 Announce Type: new Abstract: We formalize slate recommendation as a randomized score learner followed by deterministic selection. First, an appropriately scoped differential-privacy

25 Sept 2026

LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication

arXiv:2609.23193v1 Announce Type: cross Abstract: As Large Language Model (LLM) APIs become increasingly integrated into privacy-sensitive workflows, ensuring inference-time privacy without compromisi

researcharxiv-cs-cl

22 Sept 2026

FedVideoMAE: Efficient Federated Video Moderation with Differential Privacy and Secure Aggregation

arXiv:2512.18809v3 Announce Type: replace-cross Abstract: Short-form video moderation is increasingly pushed toward edge and privacy-sensitive settings, where users may intend videos for a limited aud

18 Sept 2026

Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G

arXiv:2609.09591v1 Announce Type: cross Abstract: Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence, where heterogeneous robots

11 Sept 2026

CIPHER: Benchmarking Cross-record Inference over Privacy-Hardened Evidence Records

arXiv:2609.07022v1 Announce Type: cross Abstract: Reasoning over privacy-constrained records requires combining structured attributes with evidence from free-text narratives. We introduce CIPHER (Cros

10 Sept 2026

Privacy-Preserving Split Learning for Federated LLM Fine-Tuning

arXiv:2609.09794v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on domain-specific data is essential for downstream adaptation. In many deployments, a participant cannot hold

local-aiarxiv-cs-lg

10 Sept 2026

FaceLinkGen: A Re-evaluation of Identity Leakage in Privacy-Preserving Face Recognition and Face Anonymization Systems Using Simple Distillation

arXiv:2602.02914v3 Announce Type: replace Abstract: Privacy-preserving face recognition (PPFR) hides facial appearance while retaining machine recognition, whereas perception-preserving face de-identi

9 Sept 2026

Masking Radar Cognition under Adversarial Surveillance: A Distributional Privacy Framework

arXiv:2609.07428v1 Announce Type: cross Abstract: In this article, we propose an online electronic counter-countermeasure (ECCM) framework designed to conceal the strategic decision-making processes o

9 Sept 2026

Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation

arXiv:2608.20118v1 Announce Type: cross Abstract: The detection of rare disease-associated cell subsets from high-dimensional single-cell measurements is critical for understanding diseases such as le

1 Sept 2026

SecureDrive-FL: Joint Differential Privacy and Gradient-Aware Selective Homomorphic Encryption for Federated Driver Monitoring

arXiv:2608.27108v1 Announce Type: cross Abstract: Federated Learning (FL) enables privacy-aware distributed training, yet gradient updates remain exploitable: Man-in-the-Middle (MitM) interception exp

28 Aug 2026

Privacy-Preserving Object Detection for Vision Transformer-Based Models

arXiv:2608.20712v1 Announce Type: cross Abstract: We propose a novel object detection method that enables us to protect sensitive visual information of test images. Previous studies considering visual

24 Aug 2026

Picture the Epsilon: Pursuing Identity-Level Privacy Guarantees for Images

arXiv:2608.17147v1 Announce Type: cross Abstract: Image-to-image face generators are widely used, and visual dissimilarity between their outputs and source images is sometimes treated as evidence of p

19 Aug 2026

Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs

arXiv:2608.12675v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely used to improve the performance of Large Language Models (LLMs) in answering user queries. Existing priva

14 Aug 2026

ABC: Numerical Data Collection under Local Differential Privacy without Prior Knowledge

arXiv:2608.05737v1 Announce Type: cross Abstract: Local Differential Privacy (LDP) provides strong privacy guarantees for collecting numerical data. A fundamental challenge, however, is that existing

researcharxiv-cs-ai

7 Aug 2026

Similarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentation

arXiv:2608.00872v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative training of machine learning models across multiple institutions without sharing sensitive data, making

4 Aug 2026

FedSLIM: Privacy-Preserving Federated MDL-Based Descriptive Pattern Mining Across Data Silos

arXiv:2607.23236v1 Announce Type: cross Abstract: Federated learning has achieved considerable success for predictive modelling, yet federated descriptive analytics remains largely unexplored. Existin

applicationsarxiv-cs-ai

28 Jul 2026

Cross-reality location privacy protection in 6G-enabled vehicular metaverses: an LLM-enhanced hybrid generative diffusion model-based approach

arXiv:2601.12311v2 Announce Type: replace-cross Abstract: The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through space

safetyarxiv-cs-lg

27 Jul 2026

Proximity Features: Privacy-Compliant Cold-Start Personalization at Airbnb

arXiv:2607.12246v1 Announce Type: new Abstract: Personalization in two-sided marketplaces relies heavily on user-level features, yet for platforms with infrequent, high-consideration purchases, a larg

applicationsarxiv-cs-lg

15 Jul 2026

Reducing information dependency does not cause training data privacy. Adversarially non-robust features do

arXiv:2607.12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconst

15 Jul 2026

Toward Production-Ready Federated Learning in Healthcare: Privacy, Orchestration, and Governance in MLOps

arXiv:2607.10467v2 Announce Type: replace-cross Abstract: Healthcare organizations often cannot freely centralize patient data because medical records are sensitive, regulated, and institutionally con

local-aiarxiv-cs-lg

15 Jul 2026

Agents

Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS

arXiv:2607.08402v1 Announce Type: cross Abstract: Large-scale and diverse datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportatio

agentsarxiv-cs-ai

10 Jul 2026

Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacy

arXiv:2607.06320v1 Announce Type: cross Abstract: We present the dithered Gaussian mechanism, a novel alternative to the discrete Gaussian mechanism for differential privacy that discretizes the priva

8 Jul 2026

Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability

arXiv:2503.07570v3 Announce Type: replace-cross Abstract: Deep learning, when integrated with a large amount of training data, has the potential to outperform machine learning in terms of high accurac

3 Jul 2026

Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization

arXiv:2607.00978v1 Announce Type: new Abstract: Privacy-preserving perception is a critical requirement for deploying 3D scene understanding systems in real-world indoor environments, yet it remains u

applicationsarxiv-cs-cv

2 Jul 2026

Bit-ViP: Leveraging Bit-planes to Preserve Visual Privacy in Images through Obfuscation

arXiv:2606.29417v1 Announce Type: new Abstract: The unprecedented growth of computer vision applications, such as surveillance systems and social media, raises security and visual privacy concerns, es

30 Jun 2026

Productionized Fairness Measurement Under Privacy Constraints

arXiv:2606.27558v1 Announce Type: new Abstract: Fairness measurements in the form of disaggregated evaluations often rely on demographic signals that are legally constrained or culturally sensitive. R

safetyarxiv-cs-lg

29 Jun 2026

Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data

arXiv:2606.11272v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitl

applicationsarxiv-cs-ai

11 Jun 2026

Privacy-Preserving Credit Risk Prediction with Alternative Data

arXiv:2606.10333v1 Announce Type: new Abstract: Credit risk prediction is a critical problem in the consumer credit industry. Traditionally, financial institutions construct credit risk prediction mod

applicationsarxiv-cs-lg

10 Jun 2026

SPARX: Secure and Privacy-Aware Approximate CNN Acceleration with Edge RISC-V SoC

arXiv:2606.09946v1 Announce Type: cross Abstract: Edge-AI systems increasingly require real-time CNN inference under strict energy, performance, security, and privacy constraints. Approximate computin

local-aiarxiv-cs-cv

10 Jun 2026

When Recovery Matters: The Blind Spot of Surrogate Privacy in MLLM Editing

model-releasesarxiv-cs-cv

arXiv:2606.07171v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) enable flexible instruction-driven image editing, but privacy risks arise when user images expose diverse and u

8 Jun 2026

DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data

arXiv:2606.04399v1 Announce Type: new Abstract: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server. Al

local-aiarxiv-cs-lg

4 Jun 2026

Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss

arXiv:2606.03878v1 Announce Type: cross Abstract: Advertising platforms use randomized lift tests to measure incrementality, but privacy-preserving reporting systems degrade the observed signal throug

3 Jun 2026

Private and Stable Test-Time Adaptation with Differential Privacy

arXiv:2606.01908v1 Announce Type: cross Abstract: Test-time adaptation (TTA) can reduce error on new and different data by updating the model on these inputs during inference. However, these updates r

2 Jun 2026

MosaicLeaks:Privacy Risks in Querying-in-the-Open for Deep Research Agents

model-releasesarxiv-cs-cl

arXiv:2605.30727v1 Announce Type: new Abstract: Deep research agents increasingly combine private local documents with external tools like web retrieval, creating a privacy risk: an agent's external q

1 Jun 2026

Hidden-State Privacy Has an Empty Middle

arXiv:2605.24042v1 Announce Type: cross Abstract: Of 1{,}536 Gaussian release covariances we tested for single-layer hidden-state privacy, zero achieve both moderate utility and moderate privacy again

safetyarxiv-cs-ai

26 May 2026

Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence

arXiv:2605.24239v1 Announce Type: cross Abstract: Many existing Artificial Intelligence (AI) solutions on mobile devices rely on an extensive collection of sensitive data, raising privacy concerns and

local-aiarxiv-cs-ai

26 May 2026

Thermal-Only Crowd Counting with Deployment-Time Privacy Protection

arXiv:2605.17042v1 Announce Type: new Abstract: While RGB-Thermal crowd counting has shown promise, the paradigm faces critical limitations: RGB data raises privacy concerns in public surveillance, an

applicationsarxiv-cs-cv

19 May 2026

PAAC: Privacy-Aware Agentic Device-Cloud Collaboration

arXiv:2605.08646v1 Announce Type: cross Abstract: Large language model (LLM) agents face a structural tension: cloud agents provide strong reasoning but expose user data, while on-device agents preser

local-aiarxiv-cs-cl

12 May 2026

Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable

arXiv:2605.10404v1 Announce Type: new Abstract: With the growing prevalence of always-on hardware such as smart glasses, body cameras, and home security systems, life-logging visual sensing is becomin

12 May 2026

INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy

arXiv:2605.07930v1 Announce Type: cross Abstract: Differential privacy (DP) is widely employed in machine learning to protect confidential or sensitive training data from being revealed. As data owner

researcharxiv-cs-ai

11 May 2026

Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning

arXiv:2605.04911v1 Announce Type: new Abstract: Tabular data synthesis aims to generate high-quality data while preserving privacy. However, we find that existing tabular generative models exhibit a c

applicationsarxiv-cs-lg

7 May 2026

Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States

arXiv:2506.00158v2 Announce Type: replace Abstract: Zeroth-order optimization has emerged as a promising approach for fine-tuning large language models under differential privacy (DP) and memory const

4 May 2026

Privacy-Preserving Clothing Classification using Vision Transformer for Thermal Comfort Estimation

arXiv:2604.26184v1 Announce Type: new Abstract: A privacy-preserving clothing classification scheme is presented to enable secure occupant-centric control (OCC) systems. Although the utilization of ca