Zero-trust blockchain identity model enhances data protection for expanding AIoT ecosystems

A new study proposes a privacy-preserving, high-efficiency identity management solution tailored for the Artificial Intelligence of Things (AIoT). The research is presented in the paper “Blockchain-Based Self-Sovereign Identity Management Mechanism in AIoT Environments,” published in Electronics.

The paper highlights growing risks in large-scale AIoT deployments, ranging from excessive data exposure to cumbersome permission handling, and offers a blockchain-enabled self-sovereign identity (SSI) mechanism as a practical answer. By combining selective disclosure of personal or device attributes with context-aware permission controls, the model addresses both security and operational efficiency.

Tackling privacy and permission risks in AIoT

According to the authors, traditional centralized identity management cannot meet the complex, dynamic requirements of AIoT systems, where thousands or millions of connected devices share sensitive data in real time. In such environments, over-broad permissions and unprotected attributes become targets for misuse or breaches.

To solve this, the study introduces a zero-trust-aligned SSI framework that allows devices and users to prove their identities and access rights without exposing unnecessary data. A key innovation is selective disclosure, which lets a participant share only the attributes required for a specific transaction or interaction, backed by cryptographic commitments and zero-knowledge proofs.

Complementing this is a dynamic permission management module, which continuously generates the least-privilege set of permissions based on the device’s current needs and its operational context. This approach minimizes exposure of sensitive information while keeping workflows efficient.

How the proposed mechanism works

The system’s architecture combines a blockchain ledger for recording immutable proofs, a key generation process that preserves user sovereignty, and a verification mechanism that relies on aggregated signatures. Users generate part of their cryptographic keys locally, ensuring that control over credentials remains in their own hands.

When access is requested, only the minimal necessary attributes are disclosed. Verification of these attributes is performed on the blockchain using zero-knowledge proofs, ensuring privacy even as integrity is checked. The context-aware permission engine issues just-in-time permissions, dynamically adapting to evolving AIoT scenarios such as changing sensor needs or cross-domain device interactions.

Security analysis in the study shows that the approach is resilient to common threats. The use of elliptic curve cryptography and Pedersen commitments protects both system and user keys, while the least-privilege policy reduces the attack surface. The authors emphasize that this framework aligns with zero-trust principles and reduces the risks of over-privileged access.

Demonstrated gains in privacy, speed, and scalability

The researchers tested their model in a simulated AIoT environment involving 10,000 users and 20 sensitive attributes per user, measuring performance against both full-disclosure methods and a differential-privacy baseline.

Key findings include:

• Faster operations: Selective-disclosure credentials averaged 12.3 ms for generation and 8.5 ms for verification, over 70 percent faster than full-attribute checks.

• Stronger privacy: Revealing only one attribute yielded higher uncertainty for attackers and lower success rates compared to differential-privacy methods.

• Efficient permissions: The dynamic module maintained millisecond-level latency across hundreds of policies, outperforming traditional and differential-privacy setups.

• Low overhead: Storage, processing, and network requirements remained modest even when scaling up to 10,000 users, confirming practical feasibility.

These results show that high-level privacy protection can be achieved without sacrificing operational speed or scalability, a long-standing challenge for secure AIoT deployment.

Implications for AIoT security and future work

Secure and privacy-preserving identity management is fundamental to the growth of AIoT ecosystems, from smart cities and autonomous vehicles to industrial and healthcare applications. The proposed mechanism offers a pathway to comply with global data-protection regulations while enabling seamless device-to-device and user-to-service interactions.

The authors acknowledge, however, that scaling to even larger and more complex AIoT networks will demand further optimization, particularly in how selective-disclosure proofs are computed and verified. Future research will focus on refining the cryptographic protocols for ultra-large deployments and integrating them with other trust-enhancing technologies.