DISTIL: Digital Identities for the Evaluation of Job Skills

Abstract

In today’s digital era, identity and credentials have shifted to electronic formats, offering new opportunities for secure and efficient verification. However, recruitment processes still face inefficiencies, fraud risks, and privacy concerns. This work presents the Digital IdentitieS evaluaTion of job skIILs (DISTIL) framework, which applies digital identity principles, particularly verifiable credentials, and leverages distributed ledger technology to address these issues. DISTIL employs zeroknowledge proofs to enable privacy-preserving validation of candidate data, with an emphasis on maintaining data confidentiality. We also evaluate the performance of the DISTIL framework by thoroughly examining its computational efficiency. This approach provides a secure, streamlined, and privacy-conscious solution for modern recruitment, supporting the broader adoption of selfsovereign digital identity in this field.