{
  "abstract": "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 handful of spatio-temporal points, such attacks have historically required significant manual effort from skilled analysts, limiting their practical scale. In this feasibility study, we demonstrate in a real world setting that agentic AI fundamentally changes this threat model. We present an end-to-end pipeline in which large language model agents autonomously search the open web, cross-reference public records and social media, and resolve raw coordinate sequences to candidate identities - without human intervention. We evaluate the pipeline on a spatio-temporal dataset containing simulated location points anchored at and around true home and work addresses, focusing on a high-risk disclosure scenario. Our results demonstrate that, from spatio-temporal data and public sources alone, our agentic AI successfully re-identified 18 of the 25 re-identifiable individuals (72%) and 18 of 43 cases overall (41.9%). We discuss implications for Statistical Disclosure Control (SDC) practice and outline the near-future escalation that data custodians and regulators must anticipate. De facto anonymity - an implicit foundation of SDC practice - is shifting. Agentic AI strengthens the case that re-identification is reasonably likely by any means under the GDPR Recital-26 standard, at costs of minutes-and-dollars per target.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Privacy in Statistical Databases (PSD 2026), Lecture Notes in Computer Science, vol. 16925, pp. 319-332, Springer, Cham (2027)",
  "arxiv": "2606.27936",
  "author_total": 3,
  "authors": [
    {
      "name": "Oscar Thees"
    },
    {
      "name": "Roman Müller"
    },
    {
      "name": "Matthias Templ"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ed7r1eqwjf3f1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.27936",
  "fulltext_url": null,
  "id": "ed7r1eqwjf3f1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.27936",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.CR"
    ],
    [
      "arxiv",
      "stat.AP"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2606.27936",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ed7r1eqwjf3f1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:33Z",
      "native_id": "2606.27936",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.27936"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy",
  "title_text": "Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:33Z",
  "url": "https://research.draconitic.com/paper/ed7r1eqwjf3f1",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1007/978-3-032-37883-5_21",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}