Knowledge catalogue
arXiv:2512.06380v3 Announce Type: replace-cross Abstract: Audio Large language models (LLMs) are increasingly deployed in the real world, where they inevitably capture speech from unintended nearby by
arXiv:2510.18109v4 Announce Type: replace-cross Abstract: Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model bu
arXiv:2604.15851v1 Announce Type: cross Abstract: Differential privacy (DP) has a wide range of applications for protecting data privacy, but designing and verifying DP algorithms requires expert-leve
arXiv:2604.12737v1 Announce Type: cross Abstract: While Federated Learning (FL) mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks (MIAs).
arXiv:2604.11523v1 Announce Type: new Abstract: We are entering an era in which individuals and organizations increasingly deploy dedicated AI agents that interact and collaborate with other agents. H
arXiv:2503.23001v5 Announce Type: replace Abstract: The deployment of large language models (LLMs) for next-generation network optimization introduces novel data governance challenges. mobile network
arXiv:2602.00821v2 Announce Type: replace Abstract: The secure analysis of dermatological images in clinical environments is fundamentally restricted by the critical trade-off between patient privacy
arXiv:2604.07884v1 Announce Type: new Abstract: High-fidelity generative models are increasingly needed in privacy-sensitive scenarios, where access to data is severely restricted due to regulatory an
arXiv:2604.06831v1 Announce Type: cross Abstract: Current LLM-based services typically require users to submit raw text regardless of its sensitivity. While intuitive, such practice introduces substan
Use 𝕏 Chat for messaging and voice/video calls. Comes with this great benefit of actual privacy. 🚨 WhatsApp’s “end-to-end encrypted” privacy is a total lie. New class-action lawsuit just dropped: Meta
arXiv:2610.07212v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains a language model on problems that may themselves be confidential, and the trained model can
arXiv:2610.02943v1 Announce Type: new Abstract: Multimodal 3D Human Pose Estimation (3D HPE) combines complementary information from RGB, LiDAR, and mmWave radar, but models trained on correlated obse
arXiv:2609.38458v1 Announce Type: new Abstract: Clinical large language model (LLM) agents deployed locally can consult more capable remote models, but doing so risks exposing patient information. Pri
arXiv:2609.36153v1 Announce Type: cross Abstract: B2B advertising targets a viewer's professional attributes (employer size and industry, function, seniority) and has obtained them by matching identit
arXiv:2609.28137v1 Announce Type: cross Abstract: Educational technology (EdTech) platforms collect highly sensitive student data, including behavioral logs, disability records, and academic histories
arXiv:2609.25484v1 Announce Type: new Abstract: Smart meter (SM) data provides fine-grained visibility into household energy consumption, but also exposes users to privacy risks. Inference attacks, kn
arXiv:2401.08038v2 Announce Type: replace Abstract: A significant challenge to training accurate deep learning models on privacy policies is the cost and difficulty of obtaining a large and comprehens
arXiv:2609.21686v1 Announce Type: cross Abstract: Privacy leakage in LLM agents is commonly evaluated within individual components such as memory, retrieval, or tool-use pipelines, which makes it diff
arXiv:2609.15871v1 Announce Type: cross Abstract: Rising societal and lifestyle complexity has been linked to a growing prevalence of mental distress worldwide. Educational institutions, workplaces, c
arXiv:2609.15950v1 Announce Type: new Abstract: Record-level differential privacy exposes a structural misalignment in personalized federated learning when client-specific variation is low-dimensional
arXiv:2609.14778v1 Announce Type: new Abstract: We propose a decentralized privacy-preserving learning algorithm in which each agent holds a single private sample and a shared model. Samples are learn
arXiv:2403.05864v3 Announce Type: replace Abstract: In human-centric Cyber-Physical Systems (CPS), personalized Deep Reinforcement Learning (DRL) agents must share fine-grained control actions with cl
arXiv:2609.11762v1 Announce Type: new Abstract: Per-layer differential privacy (DP) clipping improves gradient fidelity in federated learning by allocating per-matrix clipping budgets proportional to
arXiv:2310.10092v4 Announce Type: replace Abstract: This paper explores the use of linear aggregation to protect the privacy of sensitive training labels through the concept of label differential priv
arXiv:2603.04199v2 Announce Type: replace-cross Abstract: Theoretical and applied research into privacy encompasses an incredibly broad swathe of differing approaches, emphases and aims. This work int
arXiv:2606.14518v2 Announce Type: replace Abstract: The removal of learned data from Machine Learning models through Machine Unlearning (MU) has been widely studied; however, there is no agreed-upon s
arXiv:2609.07623v1 Announce Type: cross Abstract: Dot maps, which visualize individual data points as dots over a geographic region, are widely used across diverse domains to represent spatial pattern
arXiv:2609.02967v1 Announce Type: cross Abstract: Topology-guided safeguards for LLM-based multi-agent systems (MAS) train a GNN over the inter-agent communication graph to localize risky agents and i
arXiv:2509.10882v2 Announce Type: replace Abstract: In high-stakes domains such as healthcare, privacy concerns severely limit the use of real-world training data. Differentially private (DP) syntheti
arXiv:2608.27782v1 Announce Type: cross Abstract: Memorization in large language models is measured through a zoo of definitions whose formal relations are unknown, and differential privacy (DP) is tr
arXiv:2410.07632v3 Announce Type: replace Abstract: We study what provable privacy attacks can be shown for trained 2-layer ReLU neural networks, focusing on two types of attacks: membership inference
arXiv:2603.08913v2 Announce Type: replace Abstract: Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction
arXiv:2608.04255v1 Announce Type: cross Abstract: Graph inference over relational data can expose sensitive edge information, and this risk becomes more severe in dynamic graphs, where repeated model
arXiv:2608.05115v1 Announce Type: cross Abstract: Can computer vision help make classrooms safer? In this pilot study, we investigate privacy-aware and computationally efficient classroom incident rec
arXiv:2608.05127v1 Announce Type: cross Abstract: Achieving local differential privacy in distributed optimization while maintaining low communication cost remains challenging. Existing vector quantiz
arXiv:2607.19580v1 Announce Type: new Abstract: Differentially private machine learning enables model training on sensitive data while ensuring that individual data is unlikely to be recoverable from
arXiv:2607.19400v1 Announce Type: new Abstract: Pre-trained foundation models (FMs) have begun transforming single-cell genomics, but scaling them raises privacy concerns. Moreover, unlike text data,
arXiv:2607.13088v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly moving from research settings into the wild, deployed on enterprise infrastructure, personal devices, and edg
arXiv:2607.13541v1 Announce Type: cross Abstract: To overcome data scarcity and privacy constraints in data collection, it has become standard practice across academia and industry to augment real tra
arXiv:2606.26627v1 Announce Type: cross Abstract: Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a us
arXiv:2606.24408v1 Announce Type: new Abstract: Assessing the privacy of large language models (LLMs) presents significant challenges. In particular, most existing methods for auditing differential pr
arXiv:2606.09908v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challen
arXiv:2603.14778v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enables large language models to use external knowledge, but outsourcing the RAG service raises privacy c
arXiv:2605.28078v1 Announce Type: cross Abstract: We design a class of additive noise mechanisms that satisfy ((arepsilon, elta))-differential privacy (DP) for scalar, real-valued query functions with
arXiv:2605.21421v1 Announce Type: new Abstract: Motion capture is the gold standard for measuring human movement, but clinical use remains limited by cost, technical complexity, and privacy concerns.
arXiv:2605.19847v1 Announce Type: cross Abstract: Multi-tenant retrieval-augmented generation (RAG) services advertise per-account differential privacy as the operative leakage boundary: each account'
arXiv:2605.19032v1 Announce Type: new Abstract: Photos of faces uploaded online are vulnerable to malicious actors who can scrape facial images from online sources and intrude on personal privacy via
arXiv:2605.18246v1 Announce Type: cross Abstract: We study reinforcement learning (RL) in multi-dimensional continuous state and action spaces with one-sided feedback, where the agent receives partial
arXiv:2505.05707v2 Announce Type: replace Abstract: The integration of AI into daily life has generated considerable attention and excitement, while also raising concerns about automating algorithmic
arXiv:2510.07136v2 Announce Type: replace-cross Abstract: We study spectral graph clustering under edge differential privacy. We propose a matrix shuffling mechanism that combines randomized edge flip
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