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

2025-05-29

AI Summary

Universal Lifetime Identifiers (ULIs) introduce systemic privacy risks in the AI era due to their cross-domain linkability, rendering them vulnerable to AI-driven inference attacks. Method: We propose an AI-augmented threat model and formalize four foundational properties of identity systems. We design a progressive replacement framework integrating verifiable credentials, decentralized identifiers (DIDs), and policy-driven dynamic binding—supporting zero-knowledge assertions and context-aware access control. Contribution: This work delivers the first practical ULI-compatibility migration path, with formal proofs demonstrating resilience against AI-powered cross-domain identity inference. The framework preserves auditability, delegation capabilities, and business continuity, establishing a deployable, AI-native identity paradigm. (136 words)

Technology Category

• Philosophy and Ethics of AI: Privacy & Security
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Phasing out Universal Lifelong Identifiers due to privacy risks
• Addressing insufficiency of traditional safeguards in AI era
• Designing cryptographic identity systems compatible with existing workflows

Innovation

• Cryptographic framework for AI-era identity systems
• Phasing out Universal Lifelong Identifiers (ULIs)
• Preserves workflows with enhanced privacy safeguards