mlsanityData to 14 Oct 2026

The End Of Universal Lifelong Identifiers: Identity Systems For The AI Era

arXiv 2506.02027 ↗First posted 29 May 2025cs.CR cs.AI cs.CYcited by 0

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Abstract (arXiv)

Many identity systems assign a single, static identifier to an individual for life, reused across domains like healthcare, finance, and education. These Universal Lifelong Identifiers (ULIs) underpin critical workflows but now pose systemic privacy risks. We take the position that ULIs are fundamentally incompatible with the AI era and must be phased out. We articulate a threat model grounded in modern AI capabilities and show that traditional safeguards such as redaction, consent, and access controls are no longer sufficient. We define core properties for identity systems in the AI era and present a cryptographic framework that satisfies them while retaining compatibility with existing identifier workflows. Our design preserves institutional workflows, supports essential functions such as auditability and delegation, and offers a practical migration path beyond ULIs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 70 references that are in the map. First posted on arXiv 29 May 2025.

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17 of 70 references.

References · 17

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2022Locating and Editing Factual Associations in GPTcited by 2,691
  3. 2020Extracting Training Data from Large Language Modelscited by 2,189
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  10. 2022Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacycited by 100
  11. 2024VHELM: A Holistic Evaluation of Vision Language Modelscited by 53
  12. 2024Privacy-Preserving Instructions for Aligning Large Language Modelscited by 35
5 more
  1. 2024Teach LLMs to Phish: Stealing Private Information from Language Modelscited by 32
  2. 2023Identifying and Mitigating Privacy Risks Stemming from Language Models: A Surveycited by 30
  3. 2024HTR-VT: Handwritten Text Recognition with Vision Transformercited by 24
  4. 2023Quantifying and Analyzing Entity-level Memorization in Large Language Modelscited by 18
  5. 2025The Mighty ToRR: A Benchmark for Table Reasoning and Robustnesscited by 10

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