Privacy-Preserving AI-Enabled Decentralized Learning and Employment Records System

2026-01-06

arXiv.org

AI Summary

This work addresses the limitations of existing blockchain-based learning and employment record systems, which struggle to automatically generate skill credentials, integrate unstructured learning evidence, and mitigate risks of privacy leakage and hiring bias. We propose the first decentralized system that combines natural language processing with verifiable credentials within a Trusted Execution Environment (TEE). The system automatically extracts skills from both formal and informal learning materials to produce self-issued, cryptographically verifiable skill credentials. By leveraging a Syllabus-to-O*NET mapping and a selective disclosure mechanism, it enables unbiased job matching while ensuring that raw data and private keys never leave the secure enclave. Formal security guarantees are provided, and experiments demonstrate stable skill extraction (top skill variance <5%), supporting privacy-preserving credential verification and intelligent employment matching.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Privacy

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Learning and Employment Record (LER) systems are emerging as critical infrastructure for securely compiling and sharing educational and work achievements. Existing blockchain-based platforms leverage verifiable credentials but typically lack automated skill-credential generation and the ability to incorporate unstructured evidence of learning. In this paper,a privacy-preserving, AI-enabled decentralized LER system is proposed to address these gaps. Digitally signed transcripts from educational institutions are accepted, and verifiable self-issued skill credentials are derived inside a trusted execution environment (TEE) by a natural language processing pipeline that analyzes formal records (e.g., transcripts, syllabi) and informal artifacts. All verification and job-skill matching are performed inside the enclave with selective disclosure, so raw credentials and private keys remain enclave-confined. Job matching relies solely on attested skill vectors and is invariant to non-skill resume fields, thereby reducing opportunities for screening bias.The NLP component was evaluated on sample learner data; the mapping follows the validated Syllabus-to-O*NET methodology,and a stability test across repeated runs observed<5% variance in top-ranked skills. Formal security statements and proof sketches are provided showing that derived credentials are unforgeable and that sensitive information remains confidential. The proposed system thus supports secure education and employment credentialing, robust transcript verification,and automated, privacy-preserving skill extraction within a decentralized framework.

Problem

• Learning and Employment Records
• privacy-preserving
• skill credential generation
• unstructured evidence
• screening bias

Innovation

• privacy-preserving
• trusted execution environment
• verifiable credentials
• NLP-based skill extraction
• decentralized LER