# Classification Protocols with Minimal Disclosure

arXiv: https://arxiv.org/abs/2209.02690 · HTML: https://mlsanity.com/paper/2209.02690/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 1 of 1)

- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)

## Built on by (most cited first, 1 of 1)

- [Verifying Classification with Limited Disclosure](https://mlsanity.com/paper/2502.16352/) (2025)

## Abstract (arXiv)

We consider multi-party protocols for classification that are motivated by applications such as e-discovery in court proceedings. We identify a protocol that guarantees that the requesting party receives all responsive documents and the sending party discloses the minimal amount of non-responsive documents necessary to prove that all responsive documents have been received. This protocol can be embedded in a machine learning framework that enables automated labeling of points and the resulting multi-party protocol is equivalent to the standard one-party classification problem (if the one-party classification problem satisfies a natural independence-of-irrelevant-alternatives property). Our formal guarantees focus on the case where there is a linear classifier that correctly partitions the documents.