将生物特征与AI智能体标识符绑定以实现权限委托
Binding Biometrics with AI Agent Identifiers for Delegation of Authority
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中文总结 AI 辅助
本研究提出BIND框架，通过生物特征密码系统将人类生物特征与AI智能体ID及权限范围绑定，实现经认证的权限委托，基于人脸特征的实验验证其在零错误匹配率下96%真实匹配率，支持1024位智能体令牌。
中文摘要 AI 辅助
智能体人工智能（AI）系统的激增引发了关于AI智能体执行任务问责制的严重问题。理想情况下，AI智能体未经人类操作员明确授权不得执行关键任务。由于生物特征识别是身份认证最可靠的方法之一，它有潜力实现向AI智能体的经认证权限委托。在本研究中，我们提出了一种名为BIND的框架，该框架利用生物特征密码系统领域的思想，在智能体授权时将人类用户的生物特征数据与AI智能体身份（ID）及权限范围（特定任务约束）安全绑定。该令牌/标识符可由AI智能体提交给身份审计员，审计员同时进行生物特征认证并恢复智能体ID和权限范围，从而实现实时用户认证，并建立不可否认的人类控制及权限委托证明。我们还基于使用标准深度神经网络模型提取的人脸特征，提供了所提出的BIND框架的实际实现。为便于该实现，我们提出了一个特征适配模块，该模块将实值特征嵌入转换为适合基于涡轮纠错码的模糊承诺构造的固定长度二进制表示。实验证明了所提出的人脸密码系统的实际可行性，在零错误匹配率下达到96%的真实匹配率，并支持1024位智能体令牌。
英文摘要
The proliferation of agentic artificial intelligence (AI) systems has raised serious questions about the accountability for tasks performed by AI agents. Ideally, an AI agent must not be allowed to perform critical tasks without explicit authorization by a human operator. Since biometric recognition is one of the most reliable approaches for authenticating individuals, it has the potential to enable authenticated delegation of authority to AI agents. In this work, we present a framework called BIND, which leverages ideas from the field of biometric cryptosystems, to securely bind biometric data of the human user to the AI agent identity (ID) and authority scope (task-specific constraints) at the time of agent authorization. This token/identifier can be presented by the AI agent to an Identity Auditor, who simultaneously performs biometric authentication and recovers the agent ID and scope, thereby enabling real-time user authentication and establishing a non-repudiable proof of human control and delegation of authority. We also provide a practical implementation of the proposed BIND framework based on face features extracted using standard deep neural network models. To facilitate this implementation, we propose a feature adaptation module that transforms real-valued feature embeddings into fixed-length binary representations suitable for a fuzzy commitment construct based on turbo error correcting codes. Experiments demonstrate the practical feasibility of the proposed face cryptosystem, achieving a True Match Rate of $96\%$ at zero False Match Rate and supporting $1024$-bit agent tokens.
发表机构
- Michigan State University（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。
