arxivApr 16

PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?

arXiv:2601.09152v2 Announce Type: replace Abstract: Prior work on LLM-based privacy focuses on norm judgment over synthetic vignettes, rather than how people think about a specific data practice and formulate their opinions. We address this gap by designing PrivacyReasoner, an agent architecture gro

LLPR2 models #privacy #llm #artificial-intelligence Read on arxiv →

arxivApr 10bullish

Adaptive Differential Privacy for Federated Medical Image Segmentation Across Diverse Modalities

arXiv:2604.06518v1 Announce Type: cross Abstract: Large volumes of medical data remain underutilized because centralizing distributed data is often infeasible due to strict privacy regulations and institutional constraints. In addition, models trained in centralized settings frequently fail to gener

#medical-imaging #federated-learning #privacy Read on arxiv →

arxivApr 6

Communication-Efficient Distributed Learning with Differential Privacy

arXiv:2604.02558v1 Announce Type: new Abstract: We address nonconvex learning problems over undirected networks. In particular, we focus on the challenge of designing an algorithm that is both communication-efficient and that guarantees the privacy of the agents' data. The first goal is achieved thr

#machine-learning #optimization #privacy Read on arxiv →

thevergeApr 2bearish

PSA: Anyone with a link can view your Granola notes by default

If you use the AI-powered note-taking app Granola, you might want to double-check your privacy settings. Though Granola says your notes are "private by default," it makes them viewable to anyone with a link, and also uses them for internal AI training unless you opt out. Granola describes itself as

GR1 model #privacy #security #ai-training Read on theverge →