Henry M. Kim | Search arXiv

arXiv2026

OreProof: Verifiable Provenance with Limited Disclosure for Critical-Minerals Supply Chains Using Zero-Knowledge Proofs

Critical-minerals supply chains face a structural tension: regulators and buyers demand verifiable provenance, yet upstream actors are hesitant to disclose supplier identities, assay grades/yields, and prices that verification appears to require. We report a design science account of OreProof, a prototypical traceability platform addressing this verifiability-disclosure trade-off. Instantiated for gold, OreProof combines a hybrid on-chain/off-chain data model, Groth16 zero-knowledge proofs for selective disclosure, a Merkle-batched anchoring pipeline, and UNTP-aligned verifiable credentials on a public zkEVM testnet. Against a transparent baseline, directly inferable confidential attributes fell from three of four categories to none under a defined attacker model, while batched anchoring substantially improved throughput. Our contributions are the artifact prototype as well as four nascent design principles: prove over committed data rather than exposing it; credential only verifiable origin and flag unknown inputs for blended commodities; emit standards-aligned credentials from the outset; and partition disclosure by supply-chain role.

cs.CR↗

arXiv2026

Detoxifying Toxic Communication: A Design Science Approach to Responsible AI

Toxic language in digital workplaces such as pejoratives, sarcasm, condescension, and subtle incivility can erode trust, morale, and collaboration. Existing moderation tools primarily delete or block harmful messages, disrupting communication and offering no constructive resolution. This study adopts a Design Science Research approach to create a responsible AI artifact that detects and detoxifies toxic communication. The artifact integrates fine-tuned transformer-based classifiers (DistilBERT, DistilRoBERTa) with a generative detoxification model (mT0-XL-Detox-ORPO) that rewrites toxic text into semantically equivalent, non-offensive paraphrases. Technical evaluation demonstrates high accuracy in toxicity detection and strong semantic preservation in rewritten messages, supporting conversation continuity while reinforcing respectful discourse. The paper contributes design principles for responsible AI moderation that prioritize meaning preservation and fairness.

cs.CY↗