Search: “privacy”

QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning

arXiv:2606.09869v1 Announce Type: cross Abstract: Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resour

10 Jun 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

SoK: Colluding Adversaries in Machine Learning Pipelines

DGX agent

arXiv:2606.10091v1 Announce Type: cross Abstract: Machine learning (ML) models are susceptible to various security, privacy, and fairness risks. Adversaries with different characteristics (i.e., objec

safetyarxiv-cs-lg

10 Jun 2026

SPACE: Source-free Proxy Anchor Concept Erasure for MLLMs

arXiv:2606.09868v1 Announce Type: cross Abstract: As Multimodal Large Language Models (MLLMs) face growing privacy risks and regulatory constraints, machine unlearning (MU) has emerged as a crucial so

10 Jun 2026

Differentially Private Synthetic Data via APIs 4: Tabular Data

arXiv:2606.08259v1 Announce Type: new Abstract: This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive d

applicationsarxiv-cs-lg

Model Multiplicity for Adversarial Detection in Small Language Model Training on Edge Devices

arXiv:2606.07857v1 Announce Type: cross Abstract: The rise of edge-based machine learning has enabled distributed adaptation of language models across mobile and IoT devices, offering privacy preserva

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation

arXiv:2603.11917v2 Announce Type: replace Abstract: Real-time, on-device segmentation is critical for latency-sensitive and privacy-aware applications such as smart glasses and Internet-of-Things devi

local-aiarxiv-cs-cv

Tutorials

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning

arXiv:2603.10823v2 Announce Type: replace-cross Abstract: Deep generative models can help with data scarcity and privacy by producing synthetic training data, but they struggle in low-data, imbalanced

tutorialsarxiv-cs-lg

Synthetic but Not Realistic: The Evaluation Challenge in Generative Modelling for Structured Electronic Medical Records

arXiv:2606.08903v1 Announce Type: new Abstract: Synthetic healthcare data are widely proposed as privacy-preserving substitutes for real patient data, yet their evaluation remains dominated by statist

applicationsarxiv-cs-lg

X-Palm: Paired Multispectral-to-Smartphone Dataset for Cross-Domain Palmprint Authentication

arXiv:2606.08437v1 Announce Type: cross Abstract: Palmprint modality offers a privacy-preserving biometric solution, yet its deployment is hindered by the domain gap between controlled enrollment and

applicationsarxiv-cs-cv

From Vision to Text: A Compact Multimodal Approach for Robust, Cross-Domain Presentation Attack Detection on ID Cards

model-releasesarxiv-cs-cv

arXiv:2606.06966v1 Announce Type: new Abstract: Cross-domain shifts challenge Presentation Attack Detection (PAD) on ID Cards, given the restricted data available due to privacy concerns. This work pr

Introducing the Third Generation of Apple’s Foundation Models

local-aiapple-ml-research

Our next generation of Apple Intelligence is centered around our users, integrated deeply into our operating systems, and powered by a bold new architecture with privacy at its core. At the heart of t

Model Recycling Framework for Multi-Source Data-Free Supervised Transfer Learning

arXiv:2508.02039v2 Announce Type: replace Abstract: Increasing concerns for data privacy and other difficulties associated with retrieving source data for model training have created the need for sour

STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation

arXiv:2606.07036v1 Announce Type: cross Abstract: Synthetic histopathology image generation addresses critical challenges in computational pathology, including patient privacy and the growing need for

The Capacity of Information-Theoretic Secure Aggregation in Federated Learning

arXiv:2606.07277v1 Announce Type: cross Abstract: Secure aggregation allows a server to aggregate users' local updates while preserving update privacy. Existing information-theoretic problems typicall

local-aiarxiv-cs-lg

Towards Efficient and Exact Forgetting Services in Pre-Trained-Model-based Continual Learning

arXiv:2505.12239v2 Announce Type: replace-cross Abstract: In Continual Learning (CL), using a Pre-Trained Model (PTM) as the feature extractor has become a popular practice. Accompanied by analytic cl

Where Rectified Flows Leak: Characterising Membership Signals Along the Interpolation Path

arXiv:2606.07271v1 Announce Type: cross Abstract: Understanding what generative models retain from training data remains challenging, with implications for copyright and privacy. Beyond verbatim repro

Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems

arXiv:2606.05701v1 Announce Type: cross Abstract: The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things (IoT) technologies, and edge-based architectures ha

6 Jun 2026

Compositional Boundaries for Density Fusion

arXiv:2606.05871v1 Announce Type: cross Abstract: Distributed uncertainty-management systems often combine local probabilistic models along aggregation trees chosen by communication, privacy, or sched

6 Jun 2026

even though @activegraphai is not a memory tool, it's a runtime built around memory so it can do well there cool to see a third party verify…

model-releasesyohei-nakajima--x

even though @activegraphai is not a memory tool, it's a runtime built around memory so it can do well there cool to see a third party verify this and see that activegraph does well on privacy programs

5 Jun 2026

LLM-Conditioned Synthesis of Pathological Gaits via Structured Gait-Language Representations

arXiv:2606.06048v1 Announce Type: new Abstract: Pathological gait datasets remain scarce due to privacy, recruitment, cost, and movement variability. Our work presents a multimodal LLM-guided framewor

researcharxiv-cs-cv

5 Jun 2026

Hardware

TITAN-FedAnil+: Trust-Based Adaptive Blockchain Federated Learning for Resource-Constrained Intelligent Enterprises

arXiv:2606.04388v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as an effective paradigm for collaborative intelligence while preserving data privacy. However, data heterogeneity

hardwarearxiv-cs-ai

4 Jun 2026

Updates to Legal Terms

Vercel announced updates to its legal terms and conditions in June 2026, likely covering changes to their service agreements, privacy policies, or terms of service. This update would apply to users of

applicationsvercel-blog

4 Jun 2026

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data

arXiv:2606.03209v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) in privacy-sensitive and resource-constrained environments remains challenging. Since training data are often d

Geometry-Aware Tabular Diffusion

arXiv:2606.02607v1 Announce Type: cross Abstract: Tabular synthesis is critical for privacy-preserving sharing and augmentation, yet diffusion models rely on implicit mechanisms to capture inter-colum

ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models

arXiv:2511.19959v2 Announce Type: replace Abstract: Federated learning (FL) has been extensively studied as a privacy-preserving training paradigm. Recently, federated block coordinate descent scheme

local-aiarxiv-cs-lg

vLLM Semantic Router: Signal Driven Decision Routing for Mixture-of-Modality Models

arXiv:2603.04444v3 Announce Type: replace-cross Abstract: As large language models (LLMs) diversify across modalities, capabilities, and cost profiles, the problem of intelligent request routing -- se

Boosting Multimodal Federated Learning via Chained Modality Optimization

arXiv:2606.01856v1 Announce Type: cross Abstract: Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative learning across decentralized clients with heterogeneous data and modali

Differentially Private Datastore Generation for Retrieval-Augmented Inference

arXiv:2606.01413v1 Announce Type: cross Abstract: It is crucial for modern on-device AI systems that rely on retrieval-augmented inference to release and share datastores without compromising individu

Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering

arXiv:2303.04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients. However, FL suffe

researcharxiv-cs-lg

Safety

How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning

arXiv:2606.02119v1 Announce Type: cross Abstract: Machine unlearning aims to remove the influence of specific forget training data due to privacy, copyright or bias concerns while maintaining the mode

safetyarxiv-cs-ai

A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models

arXiv:2605.31080v1 Announce Type: cross Abstract: Blind and low-vision (BLV) audiences remain underserved by visual art descriptions, particularly across languages and in museum settings where privacy

Divergence Decoding: Inference-Time Unlearning via Auxiliary Models

arXiv:2605.31293v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently memorize sensitive training data thereby creating significant privacy and copyright risks. Addressing these risk

researcharxiv-cs-cl

Gap-K%: Measuring Top-1 Prediction Gap for Detecting Pretraining Data

arXiv:2601.19936v2 Announce Type: replace-cross Abstract: The opacity of massive pretraining corpora in Large Language Models (LLMs) raises significant privacy and copyright concerns, making pretraini

Gemini’s new AI agent is about as good as Google’s demo

model-releasesthe-verge-ai

Google's new '24/7' AI agent, Gemini Spark, can be shockingly good at doing things on your behalf. But I'm not sure it's worth the financial cost and potential privacy tradeoffs. The company gave me a

GETA: Generalized Encrypted Traffic Analysis

arXiv:2605.31277v1 Announce Type: cross Abstract: Traditional traffic analysis is being fundamentally challenged by the rapid adoption of encryption, tunnelling, and privacy-preserving protocols, whic

researcharxiv-cs-lg

Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning

arXiv:2602.13069v2 Announce Type: replace-cross Abstract: On-device fine-tuning enables privacy-preserving personalization of large language models, but mobile devices impose severe memory constraints

local-aiarxiv-cs-cl

Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective

arXiv:2505.20955v4 Announce Type: replace-cross Abstract: Diffusion models have achieved tremendous success in image generation, but they also raise significant concerns regarding privacy and copyrigh

researcharxiv-cs-lg

Agents

Error as a Lens: Probing LLM Reasoning through Synthetic Misconception Generation

arXiv:2605.29007v1 Announce Type: new Abstract: Personalized tutoring, teacher training, and education research need access to targeted synthetic misconceptions, but privacy and IRB constraints make l

agentsarxiv-cs-cl

Fairness-Aware Federated Learning with Trajectory Shapley Value

arXiv:2605.30336v1 Announce Type: new Abstract: Federated learning is an emerging distributed paradigm that addresses the challenges posed by heterogeneous, privacy-sensitive data. It enables multiple

Finding DoRI: Discovery of Retained Images in Diffusion Models

arXiv:2507.16880v3 Announce Type: replace-cross Abstract: Text-to-image diffusion models (DMs) have achieved remarkable success in image generation. However, concerns about data privacy and intellectu

Agents

LangSmith LLM Gateway lets you enforce spend limits and redacts PII before requests reach the model. Not after the fact.

LangSmith LLM Gateway is a feature that enables proactive cost and privacy controls by enforcing spending limits and redacting personally identifiable information (PII) before API requests are sent to

agentsharrison-chase--x

Agents

Open Source Browser Agent That Learns and Repeats Workflows

An open-source browser with built-in AI agents that emphasizes privacy and automation, enabling task automation through natural language without coding. The browser supports multiple AI providers incl

agentsr-ollama

Representation Unlearning: Forgetting through Information Compression

arXiv:2601.21564v2 Announce Type: replace Abstract: Machine unlearning seeks to remove the influence of specific training data from a model, a need driven by privacy regulations and robustness concern

Scalable RF Simulation in Generative 4D Worlds

arXiv:2508.12176v2 Announce Type: replace-cross Abstract: Radio Frequency (RF) sensing has emerged as a powerful, privacy-preserving alternative to vision-based methods for various perception tasks. H

applicationsarxiv-cs-ai

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer

arXiv:2601.22274v2 Announce Type: replace Abstract: Real-world federated systems seldom operate on static data: input distributions drift while privacy rules forbid raw-data sharing. We study this set

applicationsarxiv-cs-lg

A Wolf in Sheep's Clothing: Targeted Routing Hijacking in Federated RAG

arXiv:2605.28112v1 Announce Type: cross Abstract: Federated Retrieval-Augmented Generation (FedRAG) is attractive for privacy-sensitive applications because raw data remain local. As a result, routing

applicationsarxiv-cs-cl

28 May 2026

Industry

Aztec Labs acquires ZKPassport maker Obsidion as age verification pressure mounts

Blockchain privacy company Aztec Labs Ltd. announced today that it has acquired Obsidion Labs Ltd., the British startup behind zero-knowledge identity protocol ZKPassport. The deal comes as government

industrysiliconangle

27 May 2026

Black-box Membership Inference Attacks on the Pre-training Data of Image-generation Models

arXiv:2605.27020v1 Announce Type: cross Abstract: The rapid advancement of diffusion-based image generation models has raised serious concerns regarding potential copyright and privacy infringements i