关键词: 物联网, 零信任, 身份认证, 物理不可克隆函数, 隐私保护

Abstract: To enable scalable IoT systems, edge computing, as a new decentralized model, is introduced into IoT scenarios. Zero trust architecture (ZTA) is wellsuited for cloudedgeend systems with blurred boundaries, offering continuous dynamic authentication and improved security. Due to their lightweight and unclonable properties, physical unclonable functions (PUFs) are often used to generate hardware fingerprint identities for devices. PUFs exploit inherent randomness introduced during hardware fabrication processes to generate unique and nonpredictable challengeresponse pairs. If an attacker collects many plaintext CRPs during continuous authentication, he may model and predict future responses, enabling machine learning attacks. This paper proposes a PUFbased authentication solution (PAMLCA). It enhances privacy protection against machine learning attacks by leveraging oblivious pseudorandom function techniques to obfuscate CRP transmission. The solution combines static and continuous multilayer dynamic verification protocols, limiting implicit trust domains within a session. Security analysis and performance comparisons demonstrate that PAMLCA offers better security, functionality, communication, and computational efficiency compared to other related solutions.

Key words: Internet of things, zero trust, identity authentication, physical unclonable function, privacy preservation