移动应用中预训练ML模型的访问控制机制

Katsuya Matsubara, Atsuya Sato
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引用次数: 0

摘要

在移动平台上利用机器学习推理已经成为现实,因为最近的智能手机可能具有高性能处理器和信号和图像处理的硬件加速器。然而,这可能会带来问题。最重要的问题之一是,一旦应用程序获得许可,任何机器学习推断都可以无限制地应用于个人数据。此外,很难理解“预训练”机器学习模型的行为,并且可以通过替换使用过的模型来透明地修改已安装应用程序的行为。本研究旨在针对移动平台的机器学习推理功能实现访问控制。在本文中,我们描述了如何验证使用的预训练模型,以及如何在Android作为目标平台的Android神经网络API中实现权限控制。并给出了一个原型实现的评估结果。
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An Access Control Mechanism for Pre-trained ML Models in Mobile Apps
Utilizing machine learning inference in mobile platforms have been made realistic since recent smartphones may have high-performance processors and hardware accelerators of signal and image processing. However, it could bring issues. One of the most significant issues is that any machine learning inferences can be unlimitedly applied to personal data which once the application got permitted to. Furthermore, it could be quite hard to understand the behavior of a ‘pre-trained’ machine learning model and the behavior of an installed application can be transparently modified afterword by replacing a used model. This research aims to implement access control against such functionalities of machine learning inference for mobile platforms. In this paper, we describe how to verify used pre-trained models and how to implement permission control at Android Neural Networks API for Android as a target platform. And we also show an evaluation result with a prototype implementation.
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