Secure Decision Forest Evaluation

TL;DR

This paper introduces a secure protocol for decision forest evaluation that ensures privacy and soundness against malicious clients, enabling confidential and trustworthy model assessments.

Contribution

It presents a novel offline/online protocol with constant rounds that protects sensitive data and prevents client bias during decision forest evaluation.

Findings

Protocol achieves privacy against malicious clients

Ensures soundness of evaluation results

Operates with constant online rounds

Abstract

Decision forests are classical models to efficiently make decision on complex inputs with multiple features. While the global structure of the trees or forests is public, sensitive information have to be protected during the evaluation of some client inputs with respect to some server model. Indeed, the comparison thresholds on the server side may have economical value while the client inputs might be critical personal data. In addition, soundness is also important for the receiver. In our case, we will consider the server to be interested in the outcome of the model evaluation so that the client should not be able to bias it. In this paper, we propose a new offline/online protocol between a client and a server with a constant number of rounds in the online phase, with both privacy and soundness against malicious clients. CCS Concepts: $\bullet$ Security and Privacy $\rightarrow$ Cryptography.