Zero-Knowledge AI: Privacy-First ML Inference in Distributed Ecosystems
Bibliographic record
Abstract
At a time when data privacy laws and cyber-attacks are on the rise, Zero-Knowledge Proofs (ZKPs) and Artificial Intelligence (AI) hold the potential of a transformational paradigm of safe (privacy-preserving) machine learning (ML) inferences. In this paper, we present a new architecture that facilitates Zero-Knowledge AI, in which sensitive data inputs and internal model parameters remain unknown during the model inference procedure across distributed ecosystems. The proposed framework can help preserve privacy standards like GDPR and HIPAA, inference accuracies, and scalability of these inferences by utilising mechanisms to observe cryptographic zero-knowledge protocols, as well as federated learning protocols. We describe the construction of ZK-friendly models to apply to neural inference pipelines, efficient zk-SNARK-based model validation, decentralized trusting schemes, and privacy-respecting model auditing. Testing over a variety of healthcare and financial datasets indicates that our Zero-Knowledge AI solution results in high privacy guarantees with limited throughput losses. The work provides a strong basis on how to implement trusted and privacy-first AI systems in the real life and distributed operating environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".