Search: “privacy”

arXiv:2605.08288v1 Announce Type: cross Abstract: Device-free localization trains models from heterogeneous wireless and visual sensors (e.g., Wi-Fi, LiDAR) distributed across edge devices. Federated

arXiv:2508.10880v3 Announce Type: replace-cross Abstract: The widespread deployment of LLM-based agents is likely to introduce a critical privacy threat: malicious agents that proactively engage other

arXiv:2605.03069v1 Announce Type: cross Abstract: Federated learning (FL) lets distributed nodes train a shared model without exchanging their raw data, but in privacy-sensitive deployments medical se

ChatGPT learns about the world through training on diverse text data while implementing privacy protections that prevent it from storing personal information from individual conversations or retaining

arXiv:2605.02255v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in interactive and retrieval-augmented settings, raising significant privacy concerns. While at

arXiv:2605.01217v1 Announce Type: new Abstract: Face Recognition systems are widely deployed in real-world applications, but they also raise privacy concerns due to unauthorized collection and misuse

arXiv:2410.18717v2 Announce Type: replace Abstract: Recent advancements in artificial intelligence hold ample potential for monitoring applications using surveillance cameras. However, concerns about

arXiv:2605.01987v1 Announce Type: new Abstract: We study differential privacy (DP) in Graph Convolutional Networks (GCNs) through the framework of extit{subsampling stability}. We derive upper bounds

arXiv:2605.00562v1 Announce Type: new Abstract: The emergence of deep neural networks capable of revealing high-fidelity scene details from sparse 3D point clouds has raised significant privacy concer

arXiv:2504.18015v4 Announce Type: replace-cross Abstract: Face recognition poses serious privacy risks due to its reliance on sensitive and immutable biometric data. While modern systems mitigate priv

arXiv:2602.10139v3 Announce Type: replace-cross Abstract: Mobile Graphical User Interface (GUI) agents have demonstrated strong capabilities in automating complex smartphone tasks by leveraging multim

arXiv:2604.23245v1 Announce Type: cross Abstract: The use of Machine Learning (ML) for data-driven decision-making often relies on access to sensitive datasets, which introduces privacy challenges. Tr

This article likely discusses techniques for building web applications that integrate OpenAI's APIs while maintaining user privacy through filtering mechanisms. It probably covers implementation strat

arXiv:2601.00911v3 Announce Type: replace-cross Abstract: Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-of

OpenAI's Privacy Filter is a tool designed to help users protect sensitive information when using OpenAI's services by automatically detecting and redacting or filtering personally identifiable inform

arXiv:2503.16251v2 Announce Type: replace-cross Abstract: Federated Learning (FL) has gained prominence in machine learning applications across critical domains by enabling collaborative model trainin

arXiv:2604.17163v1 Announce Type: new Abstract: We propose PPEDCRF, a calibrated selective perturbation framework that protects background-based location privacy in released video frames against galle

arXiv:2604.16523v1 Announce Type: new Abstract: This paper proposes a novel privacy-preserving semantic segmentation method that can use independent keys for each client and image. In the proposed met

arXiv:2604.07486v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have emerged as a powerful tool for synthetic data generation. A particularly important use case is producing syn

arXiv:2604.07238v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly trained on sensitive user data, understanding the fundamental cost of privacy in language learning beco

arXiv:2502.02514v5 Announce Type: replace Abstract: Image AutoRegressive generation has emerged as a new powerful paradigm with image autoregressive models (IARs) matching state-of-the-art diffusion m

arXiv:2604.06409v1 Announce Type: cross Abstract: LLM agents increasingly draft messages on behalf of users, yet users routinely overshare sensitive information and disagree on what counts as private.

arXiv:2610.07399v1 Announce Type: new Abstract: Federated deep clustering seeks to learn clustering-friendly representations from decentralized unlabeled data while preserving client privacy. However,

arXiv:2610.05453v1 Announce Type: cross Abstract: Multimodal models are increasingly shifting toward unified architectures that understand and generate text, images, and other modalities within a shar

arXiv:2610.02716v1 Announce Type: new Abstract: Objective perturbation adds a random linear term to a regularized empirical risk and releases the exact perturbed minimizer. We study the finite computa

arXiv:2609.38630v1 Announce Type: new Abstract: Privacy redaction must remove personal information while preserving relationships expressed in text. We develop a multilingual named-entity tagger with

arXiv:2609.19226v1 Announce Type: cross Abstract: AI-mediated platforms coordinate work through LLM agents acting for different principals. In these workflows, privacy loss can be created before a fin

Mistral AI announced on 16 September 2026 that it has partnered with Mozilla to embed privacy, control, and choice features for users interacting with artificial‑intelligence–powered browsing tools. T

arXiv:2609.14003v1 Announce Type: cross Abstract: Personal AI agents built on large language models (LLMs) are increasingly given access to a user's private data and communications in order to provide

arXiv:2609.13823v1 Announce Type: new Abstract: Point cloud data face serious semantic privacy risks during acquisition, transmission, and cross-institutional sharing. Existing methods mostly rely on

arXiv:2312.11581v3 Announce Type: replace-cross Abstract: The pandemic in 2020 and 2021 had enormous economic and societal consequences, and studies show that contact tracing algorithms can be key in

Natalie Lung / Bloomberg: Some experts say Siri Recap and Live Rewind, always-listening AI features in new Apple Watches, could test eavesdropping laws despite privacy protections — Apple Inc.'s new a

arXiv:2609.04382v1 Announce Type: cross Abstract: We present a systems-security case study of a two-node split-LLM training system whose privacy evaluation passed while leaving an observable channel u

arXiv:2609.03055v1 Announce Type: new Abstract: Domestic robots rely on rich perception to operate in private homes, but privacy risk persists even when raw sensor data remain local. Structured repres

OpenMatter Network Inc. today announced the first significant expansion of its platform since its commercial launch three months ago, adding a developer kit, a gateway for managing access to artificia

arXiv:2608.29674v1 Announce Type: new Abstract: Sharing tabular data in high-stakes domains is constrained by privacy regulations. Synthetic data offer a promising alternative, but deep generative mod

arXiv:2608.27066v1 Announce Type: cross Abstract: Privacy-Enhancing Technologies (PETs) in computer vision often rely on noise or image perturbations to protect visual data while securely processing i

arXiv:2606.09590v2 Announce Type: replace Abstract: Medical language models (LMs) can memorize and reproduce protected health information, but privacy evaluations often focus on recovery of training t

arXiv:2608.18316v1 Announce Type: new Abstract: Structural Magnetic Resonance Imaging (MRI) is widely used in neuroimaging research and clinical practice, but structural MRI volumes may retain facial

Most founders copy/paste their privacy policy from Stripe or ask Claude to draft an MSA at 2:00 AM, then pray they don't get sued. You can stop doing this. We built a free, CC0-licensed legal template

arXiv:2608.13914v1 Announce Type: cross Abstract: Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for cent

arXiv:2608.14094v1 Announce Type: cross Abstract: Cloud-local LLM inference systems have the potential to use the reasoning capability of large cloud models while protecting sensitive user data on per

arXiv:2608.09049v1 Announce Type: new Abstract: Mobile app reviews are a rich, continuously renewing source of how users experience privacy and security, yet existing taxonomies of these concerns are

The intersection of medicine and AI has led to remarkable innovations. However, developers now face the thorny challenge of building robust medical AI tools that have been tested and evaluated on dive

arXiv:2607.29100v1 Announce Type: cross Abstract: Differentially private (DP) training of text-conditioned generative models suffers a utility cliff at strong privacy. We revisit this problem through

arXiv:2605.05905v2 Announce Type: replace Abstract: Objective perturbation is a standard mechanism in differentially private empirical risk minimization. In particular, Linear Objective Perturbation (