论文标题
绿色照明ML：部署中机器学习系统的机密性，完整性和可用性
Green Lighting ML: Confidentiality, Integrity, and Availability of Machine Learning Systems in Deployment
论文作者
论文摘要
安全和道德都是确保可以信任机器学习系统的核心。在生产机器学习中，通常从建立模型的人到部署模型的人有一个交接。在此交接中，负责模型部署的工程师通常并不了解模型的细节，因此，与其使用，暴露或妥协相关的潜在漏洞。在模型部署中可能不考虑模型盗窃，模型倒置或模型滥用等技术，因此，数据科学家和机器学习工程师有责任理解这些潜在风险，因此他们可以将它们与部署和托管模型的工程师交流。这是机器学习社区中的一个开放问题，为了帮助减轻此问题，需要开发用于验证模型隐私和安全性的自动化系统，这将有助于降低实施这些交接并增加其采用的无处不在的负担。
Security and ethics are both core to ensuring that a machine learning system can be trusted. In production machine learning, there is generally a hand-off from those who build a model to those who deploy a model. In this hand-off, the engineers responsible for model deployment are often not privy to the details of the model and thus, the potential vulnerabilities associated with its usage, exposure, or compromise. Techniques such as model theft, model inversion, or model misuse may not be considered in model deployment, and so it is incumbent upon data scientists and machine learning engineers to understand these potential risks so they can communicate them to the engineers deploying and hosting their models. This is an open problem in the machine learning community and in order to help alleviate this issue, automated systems for validating privacy and security of models need to be developed, which will help to lower the burden of implementing these hand-offs and increasing the ubiquity of their adoption.
