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Tag

#privacy

13 articles tagged #privacy

arxiv1d agobullish

From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG

arXiv:2605.18271v2 Announce Type: replace-cross Abstract: With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing them on-device has become essential for privacy and responsiveness. To handle the inherently personal and context-dependent nature of real-worl

EP1 model#on-device#privacy#efficiencyRead on arxiv →
arxiv1d ago

Advancing the State-of-the-Art in Empirical Privacy Auditing

arXiv:2606.10481v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization of individual training examples. Empirical privacy auditing (EPA) quantifies this risk by measuring realistic data leakage on membership inference (M

#privacy#language-models#auditingRead on arxiv →
arxivJun 1

LLM Anonymization Against Agentic Re-Identificatio

arXiv:2605.30848v1 Announce Type: cross Abstract: Agentic LLMs with web search change the threat model for text anonymization: weak contextual cues can become cross-referenceable evidence for re-identification, yet those same details also carry downstream analytic value of the text. Existing defense

AU1 model#anonymization#privacy#securityRead on arxiv →
arxivMay 28

Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure

arXiv:2605.01735v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world systems, they must support post-hoc removal of specific content to meet privacy and governance requirements. This motivates selective unlearning, which suppresses information a

#unlearning#large-language-models#privacyRead on arxiv →
arxivMay 28bearish

When Helpful Context Leaks: Privacy Risks in Domain-Adapted ASR

arXiv:2605.28211v1 Announce Type: new Abstract: SpeechLLMs are increasingly deployed in professional settings where domain customisation is standard practice: users supply context in prompts with sensitive information, fine-tune on proprietary recordings, or both. We identify and systematically inve

#privacy#customisation#speechRead on arxiv →
arxivMay 11

Searching for Privacy Risks in LLM Agents via Simulation

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 others in multi-turn interactions to extract sensitive information. However, the evolving nature of such

LL1 model#privacy#security#language-modelsRead on arxiv →
arxivMay 8bullish

Addressing Labelled Data Scarcity: Taxonomy-Agnostic Annotation of PII Values in HTTP Traffic using LLMs

arXiv:2605.06305v1 Announce Type: new Abstract: Automated privacy audits of web and mobile applications often analyse outbound HTTP traffic to detect Personally Identifiable Information (PII) leakage. However, existing learning-based detectors typically depend on scarce, manually labelled traffic an

LA1 model#privacy#security#annotationRead on arxiv →
arxivMay 7

Unlearning What Matters: Token-Level Attribution for Precise Language Model Unlearning

arXiv:2605.00364v2 Announce Type: replace Abstract: Machine unlearning has emerged as a critical capability for addressing privacy, safety, and regulatory concerns in large language models (LLMs). Existing methods operate at the sequence level, applying uniform updates across all tokens despite only

LLTOWM3 models#machine-unlearning#language-models#privacyRead on arxiv →
techcrunchMay 2bullish

The best AI dictation apps, tested and ranked

AI-powered dictation apps are useful for replying to emails, taking notes, and even coding through your voice

LASP2 models#dictation#speech-to-text#productivityRead on techcrunch →
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-intelligenceRead 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#privacyRead 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#privacyRead 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-trainingRead on theverge →
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