Search: “privacy”

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

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

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

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

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

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

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

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 (

arXiv:2607.28191v1 Announce Type: cross Abstract: Federated learning enables multiple institutions to train shared models without exchanging raw clinical EEG data, but it does not fully prevent privac

arXiv:2607.26283v1 Announce Type: new Abstract: Collaborative Perception (CP) improves autonomous systems' awareness of their surroundings by sharing sensor data, intermediate features, and detection

arXiv:2607.22695v1 Announce Type: new Abstract: Large Language Models (LLMs) are capable of generalizing human language for the completion of never-before-seen tasks, leading to widespread deployment.

arXiv:2607.21417v1 Announce Type: new Abstract: Federated prompt tuning (FPT) enables collaborative adaptation of vision--language models (VLMs) using lightweight prompts. Existing methods often addre

arXiv:2607.19403v1 Announce Type: cross Abstract: Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic en

arXiv:2607.19532v1 Announce Type: cross Abstract: Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models,

arXiv:2607.04698v1 Announce Type: new Abstract: The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preser

arXiv:2607.02903v1 Announce Type: cross Abstract: Explainability is central to building trustworthy AI, yet explanation interfaces can inadvertently provide adversaries with an expanded privacy-relate

arXiv:2601.14660v2 Announce Type: replace-cross Abstract: Agentic Large Language Models (LLMs) are models able to reason, plan, and execute tools over unstructured data. These abilities are enabling t

arXiv:2606.28479v1 Announce Type: cross Abstract: CSIRTs increasingly fine tune language models on vulnerability scan records, but these records expose internal network topology and create privacy ris

arXiv:2606.29673v1 Announce Type: new Abstract: Cooperative localization using range-based measurements is critical for multi-robot systems operating in GPS-denied and unstructured environments. Howev

arXiv:2606.26021v1 Announce Type: cross Abstract: Tabular foundation models are commonly assumed to present limited privacy concerns as they are often pre-trained on large collections of synthetic dat

arXiv:2606.23096v1 Announce Type: new Abstract: Minimax risk and regret are expectation-based criteria and do not capture rare but consequential failures. To address this concern, we develop a elta-ex

arXiv:2606.07585v1 Announce Type: cross Abstract: This thesis addresses group emotion recognition (GER) in-the-wild with a focus on privacy preservation. Unlike traditional emotion recognition methods