Privacy-Preserving AI-Enabled Decentralized Learning and Employment Records System
Yuqiao Xu, Mina Namazi, Sahith Reddy Jalapally, Osama Zafar, Youngjin Yoo, Erman Ayday
TL;DR
This work introduces a privacy-preserving, AI-enabled decentralized Learning and Employment Records (LER) system that uses secure enclaves, decentralized identifiers (DIDs), and verifiable credentials (VCs) to securely derive skill vectors from both formal transcripts and informal evidence. An NLP pipeline inside the enclave maps records to standardized skills via the Course--Skill Atlas and the O*NET taxonomy, enabling skills-only job–candidate matching that preserves confidentiality and reduces bias. The framework supports selective disclosure, enclave-bound provenance, and robust revocation/freshness, with formal security statements asserting unforgeability and data confidentiality. Experimental results demonstrate the practicality of enclave-backed processing, showing favorable performance and scalability on representative workloads, and discuss a hybrid path integrating zero-knowledge proofs for predicate privacy alongside TEEs.
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.