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Related Articles from SNS
Utility-Preserving De-Identification for Math Tutoring: Investigating Numeric Ambiguity in the MathEd-PII Benchmark Dataset
arXiv:2602.16571v3 Announce Type: replace Abstract: Large-scale sharing of dialogue data is key to advancing the science of teaching and learning, yet rigorous de-identification remains a major barrier. In mathematics tutoring transcripts, numeric expressions frequently resemble structured identifiers (e.g., dates or IDs), leading generic Personally Identifiable Information (PII) detection systems to over-redact core instructional content and reduce data utility. This work asks how to detect...
Compliance-Scored Best-of-N Guardrail Orchestration for Multimodal Document Generation in Payments Dispute Defense
arXiv:2606.01513v1 Announce Type: new Abstract: High-stakes enterprise document generation, including financial dispute narratives, compliance notices, and audit summaries, demands schema correctness, policy compliance, and low-latency operation at scale. Prior to a unified guardrail layer, production systems often stitched together separate PII redaction, content moderation, and format validation steps, leading to fragmented logic, slower request paths, and higher operational cost. We...
Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation
arXiv:2605.17034v2 Announce Type: replace Abstract: Standard PII filters often miss contextual data leakage in RAG systems, such as non-regulated attribute clusters that collectively identify individuals. We introduce a Privacy Policy Enforcement (PPE) framework using dual one-class density estimators with fused text embeddings and a calibrated abstain region for out-of-distribution inputs. Using an axis-stratified, multi-LLM synthetic data pipeline across medicine, finance, and law, we...
Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation
arXiv:2606.04067v1 Announce Type: new Abstract: As LLMs become increasingly woven into everyday workflows, user queries sent to cloud hosted LLMs routinely mix task-essential content with task non-essential sensitive disclosures, yet type based PII redaction is context agnostic and may raise two issues: over disclosing untyped sensitive context and over removing answer bearing spans. We recast privacy preserving query rewriting under Contextual Integrity: a span should be forwarded only if...
MaskClaw: Edge-Side Personalized Privacy Arbitration for GUI Agents with Behavior-Driven Skill Evolution
arXiv:2605.28646v2 Announce Type: replace Abstract: GUI agents rely on screenshots to infer intent and operate across applications, but these screenshots often contain private messages, medical records, payment credentials, and workplace-specific workflows. Privacy decisions in this setting depend on task, recipient, application state, and user role, yet static PII detectors miss these boundaries and cloud-side VLM reasoning can upload the raw screen before deciding what should be protected....
Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs
arXiv:2512.04668v4 Announce Type: replace Abstract: Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified. We introduce MAMA (Multi-Agent Memory Attack), a controlled evaluation framework for comparing topology-conditioned memory leakage in multi-agent LLM systems. MAMA operates on synthetic documents containing labeled Personally Identifiable Information (PII) entities, from which we generate sanitized task...
1k Data Breaches Later, the Disclosure Lag Is Worse
Today, I loaded the 1,000th data breach into Have I Been Pwned. Reflecting on that milestone number, I pondered how to mark the occasion in writing, and what immediately came to mind was a very simple question: why is it still needed? Especially considering the emergence of privacy regulations such as GDPR and CCPA in the 12 and a half years since I started HIBP, what possible purpose does it still serve?
AI, Ashby Engineering, and the future
AI, Ashby Engineering, and the Future 15 minute read Since August 2025, more than half of the new code hitting Ashby’s production systems has been AI-generated, yet customer issues remain broadly stable. More AI-written code. We have a blip in March / April every year; these cyclical patterns aren’t relevant to explain here.