The End Of Universal Lifelong Identifiers: Identity Systems For The AI Era
- URL: http://arxiv.org/abs/2506.02027v1
- Date: Thu, 29 May 2025 19:45:39 GMT
- Title: The End Of Universal Lifelong Identifiers: Identity Systems For The AI Era
- Abstract summary: Many identity systems assign a single, static identifier to an individual for life, reused across domains like healthcare, finance, and education.<n>We take the position that ULIs are fundamentally incompatible with the AI era and must be phased out.<n>We present a cryptographic framework that satisfies them while retaining compatibility with existing identifier.
- Score: 1.9580473532948401
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- SoulAuth: An Actor-native Identity Architecture and Rust Reference Implementation for Humans and Long-lived AI Actors [3.8751567016750705]
This paper introduces Actor-native Identity and presents SoulAuth, an open-source Rust reference implementation for Humans and long-lived AIActors.<n>We argue that any subject that must persist under its own identity and remain independently attributable should have an ActorIdentity that is not replaced by an Account, Credential, Client, AuthSession, IdentityBinding, or runtime instance.
arXiv Detail & Related papers (2026-09-10T08:54:02Z) - PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models [2.260041671242786]
We study identity unlearning in Arc2Face, a state-of-the-art identity-conditioned latent diffusion model for face generation.<n>We introduce Proximity-guided Identity Unlearning (PIU), an anchor-guided framework for identity unlearning.
arXiv Detail & Related papers (2026-05-21T10:55:56Z) - AnyID: Ultra-Fidelity Universal Identity-Preserving Video Generation from Any Visual References [52.81991915377852]
We present AnyID, an ultra-fidelity identity-preservation video generation framework.<n>First, we introduce a scalable omni-referenced architecture that effectively unifies heterogeneous identity inputs into a cohesive representation.<n>Second, we propose a primary-referenced generation paradigm, which designates one reference as a canonical anchor and uses a novel differential prompt to enable precise, attribute-level controllability.
arXiv Detail & Related papers (2026-03-26T08:54:48Z) - AnyCrowd: Instance-Isolated Identity-Pose Binding for Arbitrary Multi-Character Animation [55.94507360511886]
We propose AnyCrowd, a Transformer-based video generation framework capable of scaling to an arbitrary number of characters.<n>Specifically, we first introduce an Instance-Isolated Latent Representation (IILR), which encodes character instances independently prior to DiT processing to prevent latent identity entanglement.<n>Building on this disentangled representation, we further propose Tri-Stage Decoupled Attention (TSDA) to bind identities to driving poses by decomposing self-attention into: (i) instance-aware foreground attention, (ii) background-centric interaction, and (iii) global-background coordination.
arXiv Detail & Related papers (2026-03-16T15:25:04Z) - Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs [65.6660735371212]
We present textbftextscJustAsk, a framework that autonomously discovers effective extraction strategies through interaction alone.<n>It formulates extraction as an online exploration problem, using Upper Confidence Bound--based strategy selection and a hierarchical skill space spanning atomic probes and high-level orchestration.<n>Our results expose system prompts as a critical yet largely unprotected attack surface in modern agent systems.
arXiv Detail & Related papers (2026-01-29T03:53:25Z) - European digital identity: A missed opportunity? [0.0]
We identify several issues in the design of OpenID4VCI and OpenID4VP.<n>We debunk OpenID's 'paradigm-shifting' trust-model.<n>The legislation itself cannot accommodate the promise of self-sovereign identity.
arXiv Detail & Related papers (2026-01-20T21:55:24Z) - Beyond Inference Intervention: Identity-Decoupled Diffusion for Face Anonymization [55.29071072675132]
Face anonymization aims to conceal identity information while preserving non-identity attributes.<n>We propose textbfIDsuperscript2Face, a training-centric anonymization framework.<n>We show that IDtextsuperscript2Face outperforms existing methods in visual quality, identity suppression, and utility preservation.
arXiv Detail & Related papers (2025-10-28T09:28:12Z) - What Makes You Unique? Attribute Prompt Composition for Object Re-Identification [70.67907354506278]
Object Re-IDentification aims to recognize individuals across non-overlapping camera views.<n>Single-domain models tend to overfit to domain-specific features, whereas cross-domain models often rely on diverse normalization strategies.<n>We propose an Attribute Prompt Composition framework, which exploits textual semantics to jointly enhance discrimination and generalization.
arXiv Detail & Related papers (2025-09-23T07:03:08Z) - Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned [45.44933002008943]
This white paper presents the T"UV AUSTRIA Trusted AI framework.<n>It is an end-to-end audit catalog and methodology for assessing and certifying machine learning systems.<n>Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - it translates the high-level obligations of the EU AI Act into specific, testable criteria.
arXiv Detail & Related papers (2025-09-08T17:52:08Z) - Infrastructuring Contestability: A Framework for Community-Defined AI Value Pluralism [0.0]
The proliferation of AI-driven systems presents a challenge to Human-Computer Interaction and Computer-Supported Cooperative Work.<n>Current approaches to value alignment, which rely on centralized, top-down definitions, lack the mechanisms for meaningful contestability.<n>This paper introduces Community-Defined AI Value Pluralism, a socio-technical framework that addresses this gap.
arXiv Detail & Related papers (2025-07-07T16:45:50Z) - Rethinking Data Protection in the (Generative) Artificial Intelligence Era [115.71019708491386]
We propose a four-level taxonomy that captures the diverse protection needs arising in modern (generative) AI models and systems.<n>Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline.
arXiv Detail & Related papers (2025-07-03T02:45:51Z) - A Trustworthiness-based Metaphysics of Artificial Intelligence Systems [1.0878040851638]
We introduce a theory of metaphysical identity of AI systems.<n>We do so by characterizing their kinds and introducing identity criteria.<n>Our approach suggests that the identity and persistence of AI systems is sensitive to the socio-technical context.
arXiv Detail & Related papers (2025-06-03T15:45:46Z) - A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control [7.228060525494563]
This paper posits the imperative for a novel Agentic AI IAM framework.<n>We propose a comprehensive framework built upon rich, verifiable Agent Identities (IDs)<n>We also explore how Zero-Knowledge Proofs (ZKPs) enable privacy-preserving attribute disclosure and verifiable policy compliance.
arXiv Detail & Related papers (2025-05-25T20:21:55Z) - LOKA Protocol: A Decentralized Framework for Trustworthy and Ethical AI Agent Ecosystems [0.0]
We present the novel LOKA Protocol (Layered Orchestration for Knowledgeful Agents), a unified, systems-level architecture for building ethically governed, interoperable AI agent ecosystems.<n>LOKA introduces a proposed Universal Agent Identity Layer (UAIL) for decentralized, verifiable identity; intent-centric communication protocols for semantic coordination across diverse agents; and a Decentralized Ethical Consensus Protocol (DECP) that could enable agents to make context-aware decisions grounded in shared ethical baselines.
arXiv Detail & Related papers (2025-04-15T06:51:35Z) - ChatReID: Open-ended Interactive Person Retrieval via Hierarchical Progressive Tuning for Vision Language Models [49.09606704563898]
Person re-identification is a crucial task in computer vision, aiming to recognize individuals across non-overlapping camera views.<n>We propose a novel framework ChatReID, that shifts the focus towards a text-side-dominated retrieval paradigm, enabling flexible and interactive re-identification.<n>We introduce a hierarchical progressive tuning strategy, which endows Re-ID ability through three stages of tuning, i.e., from person attribute understanding to fine-grained image retrieval and to multi-modal task reasoning.
arXiv Detail & Related papers (2025-02-27T10:34:14Z) - Fundamental Risks in the Current Deployment of General-Purpose AI Models: What Have We (Not) Learnt From Cybersecurity? [60.629883024152576]
Large Language Models (LLMs) have seen rapid deployment in a wide range of use cases.<n>OpenAIs Altera are just a few examples of increased autonomy, data access, and execution capabilities.<n>These methods come with a range of cybersecurity challenges.
arXiv Detail & Related papers (2024-12-19T14:44:41Z) - Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI.
The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees.
We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
arXiv Detail & Related papers (2024-05-10T17:38:32Z) - Camera-Incremental Object Re-Identification with Identity Knowledge
Evolution [82.64836424135886]
Object Re-identification (ReID) aims to retrieve the probe object from many gallery images by associating and collecting the identities across all camera views.
When deploying the ReID algorithm in real-world scenarios, the aspect of storage, privacy constraints, and dynamic changes of cameras would degrade its generalizability and applicability.
Treating each camera's data independently, we introduce a novel ReID task named Camera-Incremental Object Re-identification (CIOR) by continually optimizing the ReID mode from the incoming stream of the camera dataset.
arXiv Detail & Related papers (2023-05-25T10:15:29Z) - Subverting machines, fluctuating identities: Re-learning human
categorization [1.3106063755117399]
Default paradigm in AI research envisions identity with essential attributes that are discrete and static.
In stark contrast, strands of thought within critical theory present a conception of identity as malleable and constructed entirely through interaction.
arXiv Detail & Related papers (2022-05-27T03:09:25Z) - Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components.
There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed.
We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
arXiv Detail & Related papers (2020-07-13T20:30:26Z) - Style Normalization and Restitution for Generalizable Person
Re-identification [89.482638433932]
We design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains.
We propose a simple yet effective Style Normalization and Restitution (SNR) module.
Our models empowered by the SNR modules significantly outperform the state-of-the-art domain generalization approaches on multiple widely-used person ReID benchmarks.
arXiv Detail & Related papers (2020-05-22T07:15:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.