arXiv (All)
open access
Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy
The widespread collection of fine-grained location data by commercial data brokers creates a re-identification risk that is not widely recognised by the public. While prior research has established that mobility traces are highly unique and that individuals can, in principle, be identified from a ha
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy#319626
- Towards A New Approach To The Legal Definition Of Personal Data And A Jurisdictional Model Of Data Protection Law: Surpassing The Requirement For An Assessment Of Identifiability From Data With An Effects-Based Approach#1177064
- Data privacy in the era of AI: Navigating regulatory landscapes for global businesses#964751
- Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant#144165
- Singling out people without knowing their names – Behavioural targeting, pseudonymous data, and the new Data Protection Regulation#461807
Record · ID 1049892
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive