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

• URL: http://arxiv.org/abs/2601.02720v1
• Date: Tue, 06 Jan 2026 05:18:03 GMT
• Title: Privacy-Preserving AI-Enabled Decentralized Learning and Employment Records System
• Authors: Yuqiao Xu, Mina Namazi, Sahith Reddy Jalapally, Osama Zafar, Youngjin Yoo, Erman Ayday,
• Abstract summary: Learning and Employment Record (LER) systems are emerging as critical infrastructure for securely compiling and sharing educational and work achievements.<n>Existing blockchain-based platforms leverage verifiable credentials but typically lack automated skill-credential generation and the ability to incorporate unstructured evidence of learning.<n>This paper proposes a privacy-preserving, AI-enabled decentralized LER system to address these gaps.
• Score: 2.756161833954979
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Refinement Provenance Inference: Detecting LLM-Refined Training Prompts from Model Behavior [58.751981587234916]
  This paper formalizes the Refinement Provenance Inference (RPI) audit task as Refinement Provenance Inference (RPI)<n>We propose RePro, a logit-based framework that fuses teacher-forced likelihood features with logit-ranking signals.<n>During training, RePro learns a transferable representation via shadow fine-tuning, and uses a lightweight linear head to infer provenance on unseen victims without training-data access.
  arXiv  Detail & Related papers  (2026-01-05T10:16:41Z)
• Document Data Matching for Blockchain-Supported Real Estate [2.9873162504735133]
  This work presents a system that integrates optical character recognition (OCR), natural language processing (NLP), and verifiable credentials (VCs) to automate document extraction, verification, and management.<n>The approach standardizes heterogeneous document formats into VCs and applies automated data matching to detect inconsistencies, while the blockchain provides a decentralized trust layer that reinforces transparency and integrity.<n>The proposed framework demonstrates the potential to streamline real estate transactions, strengthen stakeholder trust, and enable scalable, secure digital processes.
  arXiv  Detail & Related papers  (2025-12-30T20:30:48Z)
• MathLedger: A Verifiable Learning Substrate with Ledger-Attested Feedback [0.0]
  Contemporary AI systems achieve extraordinary performance yet remain opaque and non-verifiable.<n>We introduce MathLedger, a substrate for verifiable machine cognition that integrates formal verification, cryptographic attestation, and learning dynamics.<n>The contribution is infrastructural: a working prototype of ledger-attested learning that enables auditability at scale.
  arXiv  Detail & Related papers  (2025-12-22T19:27:55Z)
• Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned [45.44933002008943]
  This white paper presents the T"UV AUSTRIA Trusted AI framework.<n>It is an end-to-end audit catalog and methodology for assessing and certifying machine learning systems.<n>Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - it translates the high-level obligations of the EU AI Act into specific, testable criteria.
  arXiv  Detail & Related papers  (2025-09-08T17:52:08Z)
• End-to-End Verifiable Decentralized Federated Learning [1.374949083138427]
  Verifiable decentralized federated learning (FL) systems combine blockchains and zero-knowledge proofs (ZKP) We propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and extending verifiability to the data source.
  arXiv  Detail & Related papers  (2024-04-19T04:43:01Z)
• On Cryptographic Mechanisms for the Selective Disclosure of Verifiable Credentials [39.4080639822574]
  Verifiable credentials are a digital analogue of physical credentials. They can be presented to verifiers to reveal attributes or even predicates about the attributes included in the credential. One way to preserve privacy during presentation consists in selectively disclosing the attributes in a credential.
  arXiv  Detail & Related papers  (2024-01-16T08:22:28Z)
• FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature [60.99054146321459]
  Federated learning allows multiple parties to collaborate in learning a global model without revealing private data. We propose a cryptographic signature-based federated learning model ownership verification scheme named FedSOV.
  arXiv  Detail & Related papers  (2023-05-10T12:10:02Z)
• Reinforcement Learning on Encrypted Data [58.39270571778521]
  We present a preliminary, experimental study of how a DQN agent trained on encrypted states performs in environments with discrete and continuous state spaces. Our results highlight that the agent is still capable of learning in small state spaces even in presence of non-deterministic encryption, but performance collapses in more complex environments.
  arXiv  Detail & Related papers  (2021-09-16T21:59:37Z)
• Decentralized Federated Learning Preserves Model and Data Privacy [77.454688257702]
  We propose a fully decentralized approach, which allows to share knowledge between trained models. Students are trained on the output of their teachers via synthetically generated input data. The results show that an untrained student model, trained on the teachers output reaches comparable F1-scores as the teacher.
  arXiv  Detail & Related papers  (2021-02-01T14:38:54Z)
• Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training [55.824641135682725]
  Domain adaptation experiments using WSJ as a source domain and TED-LIUM 3 as well as SWITCHBOARD show that up to 80% of the performance of a system trained on ground-truth data can be recovered.
  arXiv  Detail & Related papers  (2020-11-26T18:51:26Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)
• A Distributed Trust Framework for Privacy-Preserving Machine Learning [4.282091426377838]
  This paper outlines a distributed infrastructure which is used to facilitate peer-to-peer trust between distributed agents. We detail a proof of concept using Hyperledger Aries, Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs)
  arXiv  Detail & Related papers  (2020-06-03T18:06:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.