扰动输入以防止模型窃取
Perturbing Inputs to Prevent Model Stealing
摘要：
我们展示了如何扰动输入到部署在云中的机器学习服务（ML服务）可以防止模型窃取攻击。在我们的公式中，有一个ML服务，它接收用户的输入并返回模型的输出。存在对学习ML服务的参数感兴趣的攻击者。我们使用线性和逻辑回归模型来说明如何从根本上改变攻击者的估计问题，战略性地添加噪声的输入。我们表明，即使有无限的样本，攻击者也无法恢复真实的模型参数。我们专注于特征之间的权衡在攻击者的估计参数的错误与错误的ML服务的输出。
英文摘要：
We show how perturbing inputs to machine learning services (ML-service) deployed in the cloud can protect against model stealing attacks. In our formulation, there is an ML-service that receives inputs from users and returns the output of the model. There is an attacker that is interested in learning the parameters of the ML-service. We use the linear and logistic regression models to illustrate how strategically adding noise to the inputs fundamentally alters the attacker's estimation problem. We show that even with infinite samples, the attacker would not be able to recover the true model parameters. We focus on characterizing the trade-off between the error in the attacker's estimate of the parameters with the error in the ML-service's output.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
