Cyber Security Requirements for Platforms Enhancing AI Reproducibility

TL;DR

This paper introduces a cyber security evaluation framework for AI reproducibility platforms, assesses five popular platforms, and highlights security gaps while offering tailored security enhancement recommendations.

Contribution

The study presents a novel cyber security evaluation framework for AI platforms and applies it to assess and improve their security features for reproducibility.

Findings

None of the platforms fully incorporate necessary cyber security measures.

Kaggle and Codalab perform better in security, privacy, usability, and trust.

The framework is versatile and applicable beyond AI platforms.

Abstract

Scientific research is increasingly reliant on computational methods, posing challenges for ensuring research reproducibility. This study focuses on the field of artificial intelligence (AI) and introduces a new framework for evaluating AI platforms for reproducibility from a cyber security standpoint to address the security challenges associated with AI research. Using this framework, five popular AI reproducibility platforms; Floydhub, BEAT, Codalab, Kaggle, and OpenML were assessed. The analysis revealed that none of these platforms fully incorporates the necessary cyber security measures essential for robust reproducibility. Kaggle and Codalab, however, performed better in terms of implementing cyber security measures covering aspects like security, privacy, usability, and trust. Consequently, the study provides tailored recommendations for different user scenarios, including individual researchers, small laboratories, and large corporations. It emphasizes the importance of integrating specific cyber security features into AI platforms to address the challenges associated with AI reproducibility, ultimately advancing reproducibility in this field. Moreover, the proposed framework can be applied beyond AI platforms, serving as a versatile tool for evaluating a wide range of systems and applications from a cyber security perspective.