Search: “privacy”

Graph Propagated Projection Unlearning: A Unified Framework for Vision and Audio Discriminative Models

arXiv:2604.13127v1 Announce Type: new Abstract: The need to selectively and efficiently erase learned information from deep neural networks is becoming increasingly important for privacy, regulatory c

researcharxiv-cs-cv

16 Apr 2026

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Research

VeriX-Anon: A Multi-Layered Framework for Mathematically Verifiable Outsourced Target-Driven Data Anonymization

DGX agent

arXiv:2604.12431v1 Announce Type: cross Abstract: Organisations increasingly outsource privacy-sensitive data transformations to cloud providers, yet no practical mechanism lets the data owner verify

researcharxiv-cs-lg

15 Apr 2026

A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environments

arXiv:2604.11146v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, F

14 Apr 2026

Differentially Private Verification of Distribution Properties

arXiv:2604.10819v1 Announce Type: cross Abstract: A recent line of work initiated by Chiesa and Gur and further developed by Herman and Rothblum investigates the sample and communication complexity of

14 Apr 2026

DisCEdge: Distributed Context Management for Large Language Models at the Edge

arXiv:2511.22599v2 Announce Type: replace-cross Abstract: Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless na

FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shift

arXiv:2604.06795v1 Announce Type: cross Abstract: Federated Learning (FL) enables decentralized model training across multiple clients without exposing private data, making it ideal for privacy-sensit

FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs

arXiv:2604.06297v1 Announce Type: cross Abstract: Given the growing reliance on private data in training Large Language Models (LLMs), Federated Learning (FL) combined with Parameter-Efficient Fine-Tu

SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport

arXiv:2604.06631v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training while preserving data privacy, but its practical deployment is hampered by system and sta

GraphRAG-LTM: A Layered Threat Modeling Framework for Attacks on Graph Retrieval-Augmented Generation

arXiv:2610.19549v1 Announce Type: new Abstract: Graph retrieval-augmented generation (GraphRAG) uses document-derived knowledge graphs to assemble evidence for large language model (LLM) answers. Its

local-aiarxiv-cs-cl

Open-MMUnlearning: Unifying Methods and Evaluation for MLLM Unlearning

arXiv:2610.10358v2 Announce Type: replace Abstract: As multimodal large language models (MLLMs) become more capable and widely deployed, concerns about privacy and safety have become increasingly pres

safetyarxiv-cs-ai

Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance

arXiv:2610.14815v1 Announce Type: cross Abstract: Industrial IoT sensors produce continuous, high-volume data streams that predictive maintenance needs to analyse in real time. Cloud-centric architect

Trustworthy AI Must Account for Interactions

arXiv:2504.07170v3 Announce Type: replace Abstract: Trustworthy AI encompasses many aspirational aspects for aligning AI systems with human values, including fairness, privacy, robustness, explainabil

safetyarxiv-cs-lg

Agents

When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems

arXiv:2610.16039v1 Announce Type: cross Abstract: Multi-agent LLM systems (MALS) introduce a privacy-leakage problem that conventional sensitivity-based defenses miss, since information disclosed to o

agentsarxiv-cs-ai

SoK: Rethinking Membership Inference in Tabular Machine Learning

arXiv:2610.18486v1 Announce Type: new Abstract: Membership Inference Attacks (MIAs) are currently the dominant approach to evaluating privacy in machine learning applications. Despite their significan

14 Oct 2026

Stable-Plastic Subspace Construction for Differentially Private Continual LoRA

arXiv:2610.17904v1 Announce Type: new Abstract: Continual learning (CL) adapts pretrained models to a sequence of tasks while retaining knowledge acquired from earlier tasks. When task data are sensit

14 Oct 2026

The Federated Tumor Segmentation and Classification (FeTS++) Challenge

arXiv:2610.18771v1 Announce Type: new Abstract: International challenges have become the standard for developing and benchmarking biomedical image analysis methods using centralized multi-institutiona

local-aiarxiv-cs-cv

14 Oct 2026

How Private is Private? A Comparative Study for Face De-Identification

model-releasesarxiv-cs-cv

arXiv:2610.10334v1 Announce Type: new Abstract: Face de-identification (FDeID) has emerged as a critical privacy-preserving technology, yet its evaluation remains fundamentally fragmented. Existing pr

8 Oct 2026

Federated Bayesian Surveillance of Mechanical Thrombectomy Adverse Events: A Population Risk Layer for Surgical Digital Twins

model-releasesarxiv-cs-ro

arXiv:2610.08464v1 Announce Type: cross Abstract: Learned surgical simulators and world models can roll out plausible procedural futures, but they carry no grounded estimate of how often interventiona

7 Oct 2026

CCQ: A Multi-State Child Care Quality Dataset to Support AI for Children's Health Research

arXiv:2610.05863v1 Announce Type: new Abstract: High-quality child care in early life is a critical determinant of children's growth and development. Research on child care quality has been constraine

6 Oct 2026

Differentially Private Mixing of Public Datasets Improves Private Learning

arXiv:2610.06636v1 Announce Type: new Abstract: Many machine learning applications involve sensitive data and therefore require training under differential privacy (DP). However, DP training often deg

6 Oct 2026

Population Scaling or Data Dilution? Dynamics of Local Topology Evolution in Decentralized Learning

arXiv:2610.05476v1 Announce Type: new Abstract: Scaling decentralized learning changes not only the number of clients N, but also the dynamics of information propagation and consensus. We argue that t

6 Oct 2026

Hardware

Cocoon: A System Architecture for Differentially Private Training with Correlated Noises

arXiv:2510.07304v2 Announce Type: replace-cross Abstract: Machine learning (ML) models memorize and leak training data, causing serious privacy issues to data owners. Training algorithms with differen

hardwarearxiv-cs-ai

5 Oct 2026

On Unlearning for Time-series Forecasting

arXiv:2610.02865v1 Announce Type: new Abstract: Time-series forecasting is widely used in sensitive domains. Models in these settings are often trained on longitudinal user- or entity-level records, w

5 Oct 2026

In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement

arXiv:2601.22169v2 Announce Type: replace-cross Abstract: Humans are susceptible to undesirable behaviours and privacy leaks under the influence of alcohol. This paper investigates drunk language, i.e

safetyarxiv-cs-ai

2 Oct 2026

Is Weight Tying Still Beneficial for Decoder-Only LLMs in Private Settings Under DP-SGD?

arXiv:2609.40335v1 Announce Type: new Abstract: Differentially Private Stochastic Gradient Descent (DP-SGD) is a leading approach for privacy-preserving fine-tuning of large language models (LLMs). Ma

1 Oct 2026

Prototype-guided Bilateral Alignment Multimodal Federated Learning

arXiv:2609.38925v1 Announce Type: new Abstract: Multimodal federated learning (MFL) has emerged as a pivotal paradigm for leveraging distributed data to enhance model performance. However, existing me

safetyarxiv-cs-ai

1 Oct 2026

Unapologetically Distributed: A Call for Decentralized Document Analysis

arXiv:2609.39684v1 Announce Type: new Abstract: Privacy has become an increasingly important concern in the Document Analysis community, to the extent that in many environments such as archives, gover

local-aiarxiv-cs-cv

1 Oct 2026

CoSec: Benchmarking Agent Security in Communities

model-releasesarxiv-cs-ai

arXiv:2609.34790v2 Announce Type: replace-cross Abstract: LLM agents operate in persistent collaborative environments involving multiple users, communities, memories, files, and tools. Community bound

30 Sept 2026

HARISSA: Inference-Time Self-Checks for Efficient and Safe Local Language Model Deployment

arXiv:2609.38006v1 Announce Type: new Abstract: Running a language model locally offers advantages in privacy, latency, and cost, but local hardware fits only small models, which are less capable than

30 Sept 2026

Agents

ASIRF: An Agentic Framework for Context-Dependent Sensitive Information Redaction

arXiv:2609.29191v1 Announce Type: new Abstract: Sensitive information is defined by domain and intent, not a universal category, yet redaction systems such as privacy filters and named-entity recogniz

agentsarxiv-cs-ai

25 Sept 2026

Federated Learning of AnDE Classifiers

arXiv:2609.28695v1 Announce Type: new Abstract: This work presents a federated framework for training Averaged n-Dependence Estimators (AnDE) in distributed environments. The proposed method focuses o

25 Sept 2026

Temporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement Learning

arXiv:2609.30258v1 Announce Type: new Abstract: Distributed learning in embodied reinforcement-learning agents offers a degree of privacy by retaining raw sensor data on-device and transmitting only p

25 Sept 2026

Private Decentralized Optimization with Noise Reduction and Bias Correction

arXiv:2609.27658v1 Announce Type: new Abstract: Private decentralized learning is affected by sampling noise, privacy noise, and decentralized bias under heterogeneous data. We propose Private Recursi

safetyarxiv-cs-lg

24 Sept 2026

Reliable Federated TinyML Deployment for IoT Security

arXiv:2609.27202v1 Announce Type: cross Abstract: The growing deployment of Internet of Things (IoT) devices has increased the need for privacy-preserving intrusion detection systems that operate dire

24 Sept 2026

Tutorials

Towards Mitigating Excessive Forgetting in LLM Unlearning via Entanglement-Guidance with Proxy Constraint

arXiv:2508.20443v3 Announce Type: replace Abstract: Large language models (LLMs) are trained on massive datasets that may include private or copyrighted content. Due to growing privacy and ownership c

tutorialsarxiv-cs-lg

23 Sept 2026

Improved Private Sparse Covariance Estimation with Multiscale Threshold Tests

arXiv:2609.22783v1 Announce Type: new Abstract: We study differentially private covariance estimation in operator norm for mean-zero sub-Gaussian distributions with unknown covariance support and at m

22 Sept 2026

PRIVET: PRoximIty leakage detection Via Extreme value Theory

arXiv:2510.24233v2 Announce Type: replace Abstract: Deep generative models are often trained on sensitive data, such as genetic sequences, health data, or more broadly, any copyrighted, licensed or pr

21 Sept 2026

Syntheses

Wiki Lint Report — 2026-09-20

linthealth-checkautomated

Automated lint: 14 errors, 54840 warnings, 3 info

20 Sept 2026

AI Policies: Help or Hindrance? A Software Developer's Perspective

arXiv:2609.16496v1 Announce Type: cross Abstract: AI policies introduced by software organisations to mitigate LLM-related risks such as sensitive information leaks and unauthorised usage are not usef

researcharxiv-cs-ai

16 Sept 2026

Cross-Domain Inference for Human Localization: Applying Wi-Fi RSSI Data to CSI-Trained Models

arXiv:2609.17204v1 Announce Type: cross Abstract: Wi-Fi signal data can be used to compromise the privacy of individuals. While many existing approaches rely on Channel State Information (CSI), collec

16 Sept 2026

A Differentially Private Federated Proximal Optimization Framework for Customer Churn Prediction in Heterogeneous Federated Telecom Networks

arXiv:2609.12470v1 Announce Type: new Abstract: Customer churn is one of the major issues in the telecommunication industry. To predict customer churn, conventional centralized machine learning approa

14 Sept 2026

GraphProfiler: Source-Linked Sensitive Attribute Inference via Personal Knowledge Graphs

model-releasesarxiv-cs-cl

arXiv:2609.12448v1 Announce Type: new Abstract: Sensitive attributes such as age, income, and occupation can be inferred from user-generated content by aggregating indirect cues across many ordinary p

14 Sept 2026

The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices

arXiv:2609.11940v1 Announce Type: cross Abstract: The rapid diffusion of generative artificial intelligence raises privacy, latency, and performance concerns that motivate a shift toward 'local-first'

14 Sept 2026

Syntheses

Wiki Lint Report — 2026-09-13

linthealth-checkautomated

Automated lint: 12 errors, 54839 warnings, 3 info

13 Sept 2026

DNA: Differentially private Neural Augmentation for contact tracing

arXiv:2404.13381v2 Announce Type: replace Abstract: The COVID19 pandemic had enormous economic and societal consequences. Contact tracing is an effective way to reduce infection rates by detecting pot

11 Sept 2026

X-CoSD: Communication-Efficient Cross-Vocabulary Collaborative Speculative Decoding

arXiv:2609.09166v1 Announce Type: new Abstract: This paper investigates collaborative speculative decoding (CoSD), a distributed large language model (LLM) inference framework in which an on-device sm

local-aiarxiv-cs-cl

10 Sept 2026

Syntheses

Wiki Lint Report — 2026-09-09

linthealth-checkautomated

Automated lint: 12 errors, 53990 warnings, 3 info

9 Sept 2026

RAPTOR: Role-Aware Private Training for Mixture-of-Experts

arXiv:2609.05770v1 Announce Type: new Abstract: Differentially private (DP) fine-tuning methods treat sparse Mixture-of-Experts (MoE) models as a single dense block, ignoring that shared layers see al